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Volume 21
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Sensors, Volume 21, Issue 6 (March-2 2021) – 328 articles

Cover Story (view full-size image): Ionospheric models calculated by GNSS observations provide a powerful method to study spatial and temporal ionospheric TEC variations, as well as monitor ionospheric disturbances before earthquakes. Our observation and analysis of TEC variations and disturbances over Japan showed that GNSS observing seems to be very effective in finding ionospheric characteristics and forecasting natural disasters. View this paper.
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14 pages, 6779 KiB  
Article
Development of Nationwide Road Quality Map: Remote Sensing Meets Field Sensing
by Sadra Karimzadeh and Masashi Matsuoka
Sensors 2021, 21(6), 2251; https://doi.org/10.3390/s21062251 - 23 Mar 2021
Cited by 7 | Viewed by 3749
Abstract
In this study, we measured the in situ international roughness index (IRI) for first-degree roads spanning more than 1300 km in East Azerbaijan Province, Iran, using a quarter car (QC). Since road quality mapping with in situ measurements is a costly and time-consuming [...] Read more.
In this study, we measured the in situ international roughness index (IRI) for first-degree roads spanning more than 1300 km in East Azerbaijan Province, Iran, using a quarter car (QC). Since road quality mapping with in situ measurements is a costly and time-consuming task, we also developed new equations for constructing a road quality proxy map (RQPM) using discriminant analysis and multispectral information from high-resolution Sentinel-2 images, which we calibrated using the in situ data on the basis of geographic information system (GIS) data. The developed equations using optimum index factor (OIF) and norm R provide a valuable tool for creating proxy maps and mitigating hazards at the network scale, not only for primary roads but also for secondary roads, and for reducing the costs of road quality monitoring. The overall accuracy and kappa coefficient of the norm R equation for road classification in East Azerbaijan province are 65.0% and 0.59, respectively. Full article
(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)
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18 pages, 8437 KiB  
Article
Energy-Efficient Ultrasonic Water Level Detection System with Dual-Target Monitoring
by Sanggoo Kang, Dafnik Saril Kumar David, Muil Yang, Yin Chao Yu and Suyun Ham
Sensors 2021, 21(6), 2241; https://doi.org/10.3390/s21062241 - 23 Mar 2021
Cited by 5 | Viewed by 5366
Abstract
This study presents a developed ultrasonic water level detection (UWLD) system with an energy-efficient design and dual-target monitoring. The water level monitoring system with a non-contact sensor is one of the suitable methods since it is not directly exposed to water. In addition, [...] Read more.
This study presents a developed ultrasonic water level detection (UWLD) system with an energy-efficient design and dual-target monitoring. The water level monitoring system with a non-contact sensor is one of the suitable methods since it is not directly exposed to water. In addition, a web-based monitoring system using a cloud computing platform is a well-known technique to provide real-time water level monitoring. However, the long-term stable operation of remotely communicating units is an issue for real-time water level monitoring. Therefore, this paper proposes a UWLD unit using a low-power consumption design for renewable energy harvesting (e.g., solar) by controlling the unit with dual microcontrollers (MCUs) to improve the energy efficiency of the system. In addition, dual targeting to the pavement and streamside is uniquely designed to monitor both the urban inundation and stream overflow. The real-time water level monitoring data obtained from the proposed UWLD system is analyzed with water level changing rate (WLCR) and water level index. The quantified WLCR and water level index with various sampling rates present a different sensitivity to heavy rain. Full article
(This article belongs to the Section Remote Sensors)
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16 pages, 4004 KiB  
Article
Detecting Bacterial Biofilms Using Fluorescence Hyperspectral Imaging and Various Discriminant Analyses
by Ahyeong Lee, Saetbyeol Park, Jinyoung Yoo, Jungsook Kang, Jongguk Lim, Youngwook Seo, Balgeum Kim and Giyoung Kim
Sensors 2021, 21(6), 2213; https://doi.org/10.3390/s21062213 - 22 Mar 2021
Cited by 21 | Viewed by 6036
Abstract
Biofilms formed on the surface of agro-food processing facilities can cause food poisoning by providing an environment in which bacteria can be cultured. Therefore, hygiene management through initial detection is important. This study aimed to assess the feasibility of detecting Escherichia coli ( [...] Read more.
Biofilms formed on the surface of agro-food processing facilities can cause food poisoning by providing an environment in which bacteria can be cultured. Therefore, hygiene management through initial detection is important. This study aimed to assess the feasibility of detecting Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium) on the surface of food processing facilities by using fluorescence hyperspectral imaging. E. coli and S. typhimurium were cultured on high-density polyethylene and stainless steel coupons, which are the main materials used in food processing facilities. We obtained fluorescence hyperspectral images for the range of 420–730 nm by emitting UV light from a 365 nm UV light source. The images were used to perform discriminant analyses (linear discriminant analysis, k-nearest neighbor analysis, and partial-least squares discriminant analysis) to identify and classify coupons on which bacteria could be cultured. The discriminant performances of specificity and sensitivity for E. coli (1–4 log CFU·cm−2) and S. typhimurium (1–6 log CFU·cm−2) were over 90% for most machine learning models used, and the highest performances were generally obtained from the k-nearest neighbor (k-NN) model. The application of the learning model to the hyperspectral image confirmed that the biofilm detection was well performed. This result indicates the possibility of rapidly inspecting biofilms using fluorescence hyperspectral images. Full article
(This article belongs to the Special Issue Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods)
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16 pages, 6924 KiB  
Article
SSVM: An Ultra-Low-Power Strain Sensing and Visualization Module for Long-Term Structural Health Monitoring
by Suleman Khan, Jongbin Won, Junsik Shin, Junyoung Park, Jong-Woong Park, Seung-Eock Kim, Yun Jang and Dong Joo Kim
Sensors 2021, 21(6), 2211; https://doi.org/10.3390/s21062211 - 22 Mar 2021
Cited by 8 | Viewed by 3902
Abstract
Structural health monitoring (SHM) is crucial for quantitative behavioral analysis of structural members such as fatigue, buckling, and crack propagation identification. However, formerly developed approaches cannot be implemented effectively for long-term infrastructure monitoring, owing to power inefficiency and data management challenges. This study [...] Read more.
Structural health monitoring (SHM) is crucial for quantitative behavioral analysis of structural members such as fatigue, buckling, and crack propagation identification. However, formerly developed approaches cannot be implemented effectively for long-term infrastructure monitoring, owing to power inefficiency and data management challenges. This study presents the development of a high-fidelity and ultra-low-power strain sensing and visualization module (SSVM), along with an effective data management technique. Deployment of 24-bit resolution analog to a digital converter and precise half-bridge circuit for strain sensing are two significant factors for efficient strain measurement and power management circuit incorporating a low-power microcontroller unit (MCU), and electronic-paper display (EPD) enabled long-term operation. A prototype for SSVM was developed that performs strain sensing and encodes the strain response in a QR code for visualization on the EPD. For efficient power management, SSVM only activated when the trigger-signal was generated and stayed in power-saving mode consuming 18 mA and 337.9 μA, respectively. The trigger-signal was designed to be generated either periodically by a timer or intentionally by a push-button. A smartphone application and cloud database were developed for efficient data acquisition and management. A lab-scale experiment was carried out to validate the proposed system with a reference strain sensing system. A cantilever beam was deflected by increasing load at its free end, and the resultant strain response of SSVM was compared with the reference. The proposed system was successfully validated to use for long-term static strain measurement. Full article
(This article belongs to the Section Remote Sensors)
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16 pages, 5205 KiB  
Article
Analysis of the Ordinary and Extraordinary Ionospheric Modes for NVIS Digital Communications Channels
by Jordi Male, Joaquim Porte, Tomas Gonzalez, Josep M. Maso, Joan L. Pijoan and David Badia
Sensors 2021, 21(6), 2210; https://doi.org/10.3390/s21062210 - 22 Mar 2021
Cited by 8 | Viewed by 3919
Abstract
Sensor networks have become more popular in recent years, now featuring plenty of options and capabilities. Notwithstanding this, remote locations present many difficulties for their study and monitoring. High-frequency (HF) communications are presented as an alternative to satellite communications, being a low-cost and [...] Read more.
Sensor networks have become more popular in recent years, now featuring plenty of options and capabilities. Notwithstanding this, remote locations present many difficulties for their study and monitoring. High-frequency (HF) communications are presented as an alternative to satellite communications, being a low-cost and easy-to-deploy solution. Near vertical incidence skywave (NVIS) technology provides a coverage of approximately 250 km (depending on the frequency being used and the ionospheric conditions) without a line of sight using the ionosphere as a communication channel. This paper centers on the study of the ionosphere and its characteristic waves as two independent channels in order to improve any NVIS link, increasing its robustness or decreasing the size of the node antennas through the appliance of specific techniques. We studied the channel sounding of both the ordinary and extraordinary waves and their respective channels, analyzing parameters such as the delay spread and the channel’s availability for each wave. The frequency instability of the hardware used was also measured. Furthermore, the correlation coefficient of the impulse response between both signals was studied. Finally, we applied polarization diversity and two different combining techniques. These measurements were performed on a single frequency link, tuned to 5.4 MHz. An improvement on the mean bit energy-to-noise power spectral density (Eb/N0) was received and the bit error rate (BER) was achieved. The results obtained showed that the extraordinary mode had a higher availability throughout the day (15% more availability), but a delayed spread (approximately 0.3 ms mean value), similar to those of the ordinary wave. Furthermore, an improvement of up to 4 dB was achieved with the usage of polarization diversity, thus reducing transmission errors. Full article
(This article belongs to the Section Remote Sensors)
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21 pages, 14046 KiB  
Article
Optical Navigation Sensor for Runway Relative Positioning of Aircraft during Final Approach
by Antal Hiba, Attila Gáti and Augustin Manecy
Sensors 2021, 21(6), 2203; https://doi.org/10.3390/s21062203 - 21 Mar 2021
Cited by 16 | Viewed by 4838
Abstract
Precise navigation is often performed by sensor fusion of different sensors. Among these sensors, optical sensors use image features to obtain the position and attitude of the camera. Runway relative navigation during final approach is a special case where robust and continuous detection [...] Read more.
Precise navigation is often performed by sensor fusion of different sensors. Among these sensors, optical sensors use image features to obtain the position and attitude of the camera. Runway relative navigation during final approach is a special case where robust and continuous detection of the runway is required. This paper presents a robust threshold marker detection method for monocular cameras and introduces an on-board real-time implementation with flight test results. Results with narrow and wide field-of-view optics are compared. The image processing approach is also evaluated on image data captured by a different on-board system. The pure optical approach of this paper increases sensor redundancy because it does not require input from an inertial sensor as most of the robust runway detectors. Full article
(This article belongs to the Special Issue UAV-Based Sensing Techniques, Applications and Prospective)
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23 pages, 6762 KiB  
Article
Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy
by Lucas de Paula Corrêdo, Leonardo Felipe Maldaner, Helizani Couto Bazame and José Paulo Molin
Sensors 2021, 21(6), 2195; https://doi.org/10.3390/s21062195 - 21 Mar 2021
Cited by 10 | Viewed by 4342
Abstract
Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of [...] Read more.
Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies. Full article
(This article belongs to the Special Issue Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods)
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22 pages, 5665 KiB  
Article
Performance Evaluation of the Highway Radar Occupancy Grid
by Jakub Porębski and Krzysztof Kogut
Sensors 2021, 21(6), 2177; https://doi.org/10.3390/s21062177 - 20 Mar 2021
Cited by 2 | Viewed by 3773
Abstract
The quality of environmental perception is crucial for automated vehicle capabilities. In order to ensure the required accuracy, the occupancy grid mapping algorithm is often utilised to fuse data from multiple sensors. This paper focuses on the radar-based occupancy grid for highway applications [...] Read more.
The quality of environmental perception is crucial for automated vehicle capabilities. In order to ensure the required accuracy, the occupancy grid mapping algorithm is often utilised to fuse data from multiple sensors. This paper focuses on the radar-based occupancy grid for highway applications and describes how to measure effectively the quality of the occupancy map. The evaluation was performed using the novel grid pole-like object analysis method. The proposed assessment is versatile and can be applied without detailed ground truth information. The evaluation was tested with a simulation and real vehicle experiments on the highway. Full article
(This article belongs to the Special Issue Sensor Fusion for Vehicles Navigation and Robotic Systems)
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20 pages, 2265 KiB  
Article
Advancing Ground-Based Radar Processing for Bridge Infrastructure Monitoring
by Chris Michel and Sina Keller
Sensors 2021, 21(6), 2172; https://doi.org/10.3390/s21062172 - 20 Mar 2021
Cited by 40 | Viewed by 3567
Abstract
In this study, we further develop the processing of ground-based interferometric radar measurements for the application of bridge monitoring. Applying ground-based radar in such complex setups or long measurement durations requires advanced processing steps to receive accurate measurements. These steps involve removing external [...] Read more.
In this study, we further develop the processing of ground-based interferometric radar measurements for the application of bridge monitoring. Applying ground-based radar in such complex setups or long measurement durations requires advanced processing steps to receive accurate measurements. These steps involve removing external influences from the measurement and evaluating the measurement uncertainty during processing. External influences include disturbances caused by objects moving through the signal, static clutter from additional scatterers, and changes in atmospheric properties. After removing these influences, the line-of-sight displacement vectors, measured by multiple ground-based radars, are decomposed into three-dimensional displacement components. The advanced processing steps are applied exemplarily on measurements with two sensors at a prestressed concrete bridge near Coburg (Germany). The external influences are successfully removed, and two components of the three-dimensional displacement vector are determined. A measurement uncertainty of less than 0.1 mm is achieved for the discussed application. Full article
(This article belongs to the Collection Modern Radar Systems)
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19 pages, 12031 KiB  
Article
Experimental Seaborne Passive Radar
by Gustaw Mazurek, Krzysztof Kulpa, Mateusz Malanowski and Aleksander Droszcz
Sensors 2021, 21(6), 2171; https://doi.org/10.3390/s21062171 - 20 Mar 2021
Cited by 15 | Viewed by 4032
Abstract
Passive bistatic radar does not emit energy by itself but relies on the energy emitted by illuminators of opportunity, such as radio or television transmitters. Ground-based passive radars are relatively well-developed, as numerous demonstrators and operational systems are being built. Passive radar on [...] Read more.
Passive bistatic radar does not emit energy by itself but relies on the energy emitted by illuminators of opportunity, such as radio or television transmitters. Ground-based passive radars are relatively well-developed, as numerous demonstrators and operational systems are being built. Passive radar on a moving platform, however, is a relatively new field. In this paper, an experimental seaborne passive radar system is presented. The radar uses digital radio (DAB) and digital television (DVB-T) for target detection. Results of clutter analysis are presented, as well as detections of real-life targets. Full article
(This article belongs to the Special Issue Active and Passive Radars on Mobile Platforms)
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20 pages, 12269 KiB  
Article
Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014–2019 and the 2016 Kumamoto Earthquake
by Tianyang Hu, Yibin Yao and Jian Kong
Sensors 2021, 21(6), 2156; https://doi.org/10.3390/s21062156 - 19 Mar 2021
Cited by 5 | Viewed by 3197
Abstract
There are a large number of excellent research cases in Global Navigation Satellite System (GNSS) positioning and disaster prediction in Japan region, where the simulation and prediction of total electron content (TEC) is a powerful research method. In this study, we used the [...] Read more.
There are a large number of excellent research cases in Global Navigation Satellite System (GNSS) positioning and disaster prediction in Japan region, where the simulation and prediction of total electron content (TEC) is a powerful research method. In this study, we used the data of the GNSS Earth Observation Network (GEONET) established by the Geographical Survey Institute of Japan (GSI) to compare the performance of two regional ionospheric models in Japan, in which the spherical cap harmonic (SCH) model has the best performance. In this paper, we investigated the spatial and temporal variations of ionospheric TEC in Japan and their relationship with latitude, longitude, seasons, and solar activity. The results show that the TEC in Japan increases as the latitude decreases, with the highest average TEC in spring and summer and the lowest in winter, and has a strong correlation with solar activity. In addition, the observation and analysis of ionospheric disturbances over Japan before the 2016 Kumamoto earthquake and geomagnetic storms showed that GNSS observing of ionospheric TEC seems to be very effective in forecasting natural disasters and monitoring space weather. Full article
(This article belongs to the Section Remote Sensors)
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21 pages, 12190 KiB  
Article
C-UNet: Complement UNet for Remote Sensing Road Extraction
by Yuewu Hou, Zhaoying Liu, Ting Zhang and Yujian Li
Sensors 2021, 21(6), 2153; https://doi.org/10.3390/s21062153 - 19 Mar 2021
Cited by 76 | Viewed by 6750
Abstract
Roads are important mode of transportation, which are very convenient for people’s daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with [...] Read more.
Roads are important mode of transportation, which are very convenient for people’s daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with a complement UNet (C-UNet). C-UNet contains four modules. Firstly, the standard UNet is used to roughly extract road information from remote sensing images, getting the first segmentation result; secondly, a fixed threshold is utilized to erase partial extracted information; thirdly, a multi-scale dense dilated convolution UNet (MD-UNet) is introduced to discover the complement road areas in the erased masks, obtaining the second segmentation result; and, finally, we fuse the extraction results of the first and the third modules, getting the final segmentation results. Experimental results on the Massachusetts Road dataset indicate that our C-UNet gets the higher results than the state-of-the-art methods, demonstrating its effectiveness. Full article
(This article belongs to the Section Remote Sensors)
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28 pages, 15167 KiB  
Article
BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data
by Stefan Reitmann, Lorenzo Neumann and Bernhard Jung
Sensors 2021, 21(6), 2144; https://doi.org/10.3390/s21062144 - 18 Mar 2021
Cited by 33 | Viewed by 15079
Abstract
Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an [...] Read more.
Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender. Full article
(This article belongs to the Section Remote Sensors)
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14 pages, 2381 KiB  
Communication
Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas
by Zhijun Zhen, Shengbo Chen, Tiangang Yin, Eric Chavanon, Nicolas Lauret, Jordan Guilleux, Michael Henke, Wenhan Qin, Lisai Cao, Jian Li, Peng Lu and Jean-Philippe Gastellu-Etchegorry
Sensors 2021, 21(6), 2115; https://doi.org/10.3390/s21062115 - 17 Mar 2021
Cited by 61 | Viewed by 7213
Abstract
Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) [...] Read more.
Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs’ saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI. Full article
(This article belongs to the Special Issue Sensors and Forest Research)
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16 pages, 3597 KiB  
Article
Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation
by Can Chen, Luca Zanotti Fragonara and Antonios Tsourdos
Sensors 2021, 21(6), 2113; https://doi.org/10.3390/s21062113 - 17 Mar 2021
Cited by 3 | Viewed by 3107
Abstract
Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning. As a result, 3D multi-object tracking (MOT) plays a vital role in autonomous navigation. Most MOT methods use a tracking-by-detection pipeline, which includes both the object detection [...] Read more.
Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning. As a result, 3D multi-object tracking (MOT) plays a vital role in autonomous navigation. Most MOT methods use a tracking-by-detection pipeline, which includes both the object detection and data association tasks. However, many approaches detect objects in 2D RGB sequences for tracking, which lacks reliability when localizing objects in 3D space. Furthermore, it is still challenging to learn discriminative features for temporally consistent detection in different frames, and the affinity matrix is typically learned from independent object features without considering the feature interaction between detected objects in the different frames. To settle these problems, we first employ a joint feature extractor to fuse the appearance feature and the motion feature captured from 2D RGB images and 3D point clouds, and then we propose a novel convolutional operation, named RelationConv, to better exploit the correlation between each pair of objects in the adjacent frames and learn a deep affinity matrix for further data association. We finally provide extensive evaluation to reveal that our proposed model achieves state-of-the-art performance on the KITTI tracking benchmark. Full article
(This article belongs to the Special Issue Multi-Sensor Systems for Object Tracking)
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23 pages, 7312 KiB  
Article
Evaluating the Ability of Multi-Sensor Techniques to Capture Topographic Complexity
by Hannah M. Cooper, Thad Wasklewicz, Zhen Zhu, William Lewis, Karley LeCompte, Madison Heffentrager, Rachel Smaby, Julian Brady and Robert Howard
Sensors 2021, 21(6), 2105; https://doi.org/10.3390/s21062105 - 17 Mar 2021
Cited by 6 | Viewed by 4085
Abstract
This study provides an evaluation of multiple sensors by examining their precision and ability to capture topographic complexity. Five different small unmanned aerial systems (sUAS) were evaluated, each with a different camera, Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU). A [...] Read more.
This study provides an evaluation of multiple sensors by examining their precision and ability to capture topographic complexity. Five different small unmanned aerial systems (sUAS) were evaluated, each with a different camera, Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU). A lidar was also used on the largest sUAS and as a mobile scanning system. The quality of each of the seven platforms were compared to actual surface measurements gathered with real-time kinematic (RTK)-GNSS and terrestrial laser scanning. Rigorous field and photogrammetric assessment workflows were designed around a combination of structure-from-motion to align images, Monte Carlo simulations to calculate spatially variable error, object-based image analysis to create objects, and MC32-PM algorithm to calculate vertical differences between two dense point clouds. The precision of the sensors ranged 0.115 m (minimum of 0.11 m for MaRS with Sony A7iii camera and maximum of 0.225 m for Mavic2 Pro). In a heterogenous test location with varying slope and high terrain roughness, only three of the seven mobile platforms performed well (MaRS, Inspire 2, and Phantom 4 Pro). All mobile sensors performed better for the homogenous test location, but the sUAS lidar and mobile lidar contained the most noise. The findings presented herein provide insights into cost–benefit of purchasing various sUAS and sensors and their ability to capture high-definition topography. Full article
(This article belongs to the Special Issue Multi-Sensor Techniques for Topographic Mapping)
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11 pages, 19900 KiB  
Article
A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery
by Meng Li, Zhuang Tang, Wei Tong, Xianju Li, Weitao Chen and Lizhe Wang
Sensors 2021, 21(6), 2089; https://doi.org/10.3390/s21062089 - 16 Mar 2021
Cited by 18 | Viewed by 3169
Abstract
Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to [...] Read more.
Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models. Full article
(This article belongs to the Section Remote Sensors)
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17 pages, 2002 KiB  
Article
Robust LCEKF for Mismatched Nonlinear Systems with Non-Additive Noise/Inputs and Its Application to Robust Vehicle Navigation
by Rayen Ben Abdallah, Jordi Vilà-Valls, Gaël Pagès, Damien Vivet and Eric Chaumette
Sensors 2021, 21(6), 2086; https://doi.org/10.3390/s21062086 - 16 Mar 2021
Cited by 4 | Viewed by 1805
Abstract
It is well known that the standard state estimation technique performance is particularly sensitive to perfect system knowledge, where the underlying assumptions are: (i) Process and measurement functions and parameters are known, (ii) inputs are known, and (iii) noise statistics are known. These [...] Read more.
It is well known that the standard state estimation technique performance is particularly sensitive to perfect system knowledge, where the underlying assumptions are: (i) Process and measurement functions and parameters are known, (ii) inputs are known, and (iii) noise statistics are known. These are rather strong assumptions in real-life applications; therefore, a robust filtering solution must be designed to cope with model misspecifications. A possible way to design robust filters is to exploit linear constraints (LCs) within the filter formulation. In this contribution we further explore the use of LCs, derive a linearly constrained extended Kalman filter (LCEKF) for systems affected by non-additive noise and system inputs, and discuss its use for model mismatch mitigation. Numerical results for a robust tracking and navigation problem are provided to show the performance improvement of the proposed LCEKF, with respect to state-of-the-art techniques, that is, a benchmark EKF without mismatch and a misspecified EKF not accounting for the mismatch. Full article
(This article belongs to the Section Remote Sensors)
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30 pages, 21047 KiB  
Article
Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves
by Shih-Yu Chen, Chinsu Lin, Guan-Jie Li, Yu-Chun Hsu and Keng-Hao Liu
Sensors 2021, 21(6), 2077; https://doi.org/10.3390/s21062077 - 16 Mar 2021
Cited by 10 | Viewed by 3752
Abstract
The life cycle of leaves, from sprout to senescence, is the phenomenon of regular changes such as budding, branching, leaf spreading, flowering, fruiting, leaf fall, and dormancy due to seasonal climate changes. It is the effect of temperature and moisture in the life [...] Read more.
The life cycle of leaves, from sprout to senescence, is the phenomenon of regular changes such as budding, branching, leaf spreading, flowering, fruiting, leaf fall, and dormancy due to seasonal climate changes. It is the effect of temperature and moisture in the life cycle on physiological changes, so the detection of newly grown leaves (NGL) is helpful for the estimation of tree growth and even climate change. This study focused on the detection of NGL based on deep learning convolutional neural network (CNN) models with sparse enhancement (SE). As the NGL areas found in forest images have similar sparse characteristics, we used a sparse image to enhance the signal of the NGL. The difference between the NGL and the background could be further improved. We then proposed hybrid CNN models that combined U-net and SegNet features to perform image segmentation. As the NGL in the image were relatively small and tiny targets, in terms of data characteristics, they also belonged to the problem of imbalanced data. Therefore, this paper further proposed 3-Layer SegNet, 3-Layer U-SegNet, 2-Layer U-SegNet, and 2-Layer Conv-U-SegNet architectures to reduce the pooling degree of traditional semantic segmentation models, and used a loss function to increase the weight of the NGL. According to the experimental results, our proposed algorithms were indeed helpful for the image segmentation of NGL and could achieve better kappa results by 0.743. Full article
(This article belongs to the Section Remote Sensors)
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16 pages, 7163 KiB  
Article
Robust Inter-Vehicle Distance Measurement Using Cooperative Vehicle Localization
by Faan Wang, Weichao Zhuang, Guodong Yin, Shuaipeng Liu, Ying Liu and Haoxuan Dong
Sensors 2021, 21(6), 2048; https://doi.org/10.3390/s21062048 - 14 Mar 2021
Cited by 17 | Viewed by 3604
Abstract
Precise localization is critical to safety for connected and automated vehicles (CAV). The global navigation satellite system is the most common vehicle positioning method and has been widely studied to improve localization accuracy. In addition to single-vehicle localization, some recently developed CAV applications [...] Read more.
Precise localization is critical to safety for connected and automated vehicles (CAV). The global navigation satellite system is the most common vehicle positioning method and has been widely studied to improve localization accuracy. In addition to single-vehicle localization, some recently developed CAV applications require accurate measurement of the inter-vehicle distance (IVD). Thus, this paper proposes a cooperative localization framework that shares the absolute position or pseudorange by using V2X communication devices to estimate the IVD. Four IVD estimation methods are presented: Absolute Position Differencing (APD), Pseudorange Differencing (PD), Single Differencing (SD) and Double Differencing (DD). Several static and dynamic experiments are conducted to evaluate and compare their measurement accuracy. The results show that the proposed methods may have different performances under different conditions. The DD shows the superior performance among the four methods if the uncorrelated errors are small or negligible (static experiment or dynamic experiment with open-sky conditions). When multi-path errors emerge due to the blocked GPS signal, the PD method using the original pseudorange is more effective because the uncorrelated errors cannot be eliminated by the differential technique. Full article
(This article belongs to the Section Remote Sensors)
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22 pages, 4372 KiB  
Article
Experimental Verification of the Concept of Using LOFAR Radio-Telescopes as Receivers in Passive Radiolocation Systems
by Julia Kłos, Konrad Jędrzejewski, Aleksander Droszcz, Krzysztof Kulpa, Mariusz Pożoga and Jacek Misiurewicz
Sensors 2021, 21(6), 2043; https://doi.org/10.3390/s21062043 - 14 Mar 2021
Cited by 8 | Viewed by 2760
Abstract
The paper presents a new idea of using a low-frequency radio-telescope belonging to the LOFAR network as a receiver in a passive radar system. The structure of a LOFAR radio-telescope station is described in the context of applying this radio-telescope for detection of [...] Read more.
The paper presents a new idea of using a low-frequency radio-telescope belonging to the LOFAR network as a receiver in a passive radar system. The structure of a LOFAR radio-telescope station is described in the context of applying this radio-telescope for detection of aerial (airplanes) and space (satellite) targets. The theoretical considerations and description of the proposed signal processing schema for the passive radar based on a LOFAR radio-telescope are outlined in the paper. The results of initial experiments verifying the concept of a LOFAR station use as a receiver and a commercial digital radio broadcasting (DAB) transmitters as illuminators of opportunity for aerial object detection are presented. Full article
(This article belongs to the Collection Modern Radar Systems)
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20 pages, 6725 KiB  
Article
A Low-Cost Active Reflector for Interferometric Monitoring Based on Sentinel-1 SAR Images
by Guido Luzi, Pedro F. Espín-López, Fermín Mira Pérez, Oriol Monserrat and Michele Crosetto
Sensors 2021, 21(6), 2008; https://doi.org/10.3390/s21062008 - 12 Mar 2021
Cited by 19 | Viewed by 3803
Abstract
The effectiveness of radar interferometric techniques in non-urban areas can often be compromised due to the lack of stable natural targets. This drawback can be partially compensated through the installation of reference targets, characterized by a bright and stable radar response. The installation [...] Read more.
The effectiveness of radar interferometric techniques in non-urban areas can often be compromised due to the lack of stable natural targets. This drawback can be partially compensated through the installation of reference targets, characterized by a bright and stable radar response. The installation of passive corner reflectors (PCR) often represents a valid aid, but these objects are usually cumbersome, and suffer from severe weather conditions; furthermore, the installation of a PCR can be difficult and costly, especially in places with hard accessibility. Active reflectors (AR) represent a less cumbersome alternative to PCRs, while still providing a stable phase response. This paper describes the design, implementation, and test of an AR prototype, designed to operate with the Sentinel-1 synthetic aperture radar (SAR), aimed at providing a fair performance/cost benefit. These characteristics, obtained through a tradeoff between the use of off-the-shelf components and a simple architecture, can make the setup of a dense network (i.e., tens of devices) in the monitored areas feasible. The paper reports the design, implementation, and the analysis of different tests carried out in a laboratory, and in a real condition in the field, to illustrate AR reliability and estimate its phase stability. Full article
(This article belongs to the Section Remote Sensors)
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22 pages, 6311 KiB  
Article
Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images
by Qian Ma, Wenting Han, Shenjin Huang, Shide Dong, Guang Li and Haipeng Chen
Sensors 2021, 21(6), 1994; https://doi.org/10.3390/s21061994 - 12 Mar 2021
Cited by 15 | Viewed by 2557
Abstract
This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, [...] Read more.
This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing Applications in Precision Agriculture)
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23 pages, 6360 KiB  
Article
Attention-Based Context Aware Network for Semantic Comprehension of Aerial Scenery
by Weipeng Shi, Wenhu Qin, Zhonghua Yun, Peng Ping, Kaiyang Wu and Yuke Qu
Sensors 2021, 21(6), 1983; https://doi.org/10.3390/s21061983 - 11 Mar 2021
Cited by 3 | Viewed by 2436
Abstract
It is essential for researchers to have a proper interpretation of remote sensing images (RSIs) and precise semantic labeling of their component parts. Although FCN (Fully Convolutional Networks)-like deep convolutional network architectures have been widely applied in the perception of autonomous cars, there [...] Read more.
It is essential for researchers to have a proper interpretation of remote sensing images (RSIs) and precise semantic labeling of their component parts. Although FCN (Fully Convolutional Networks)-like deep convolutional network architectures have been widely applied in the perception of autonomous cars, there are still two challenges in the semantic segmentation of RSIs. The first is to identify details in high-resolution images with complex scenes and to solve the class-mismatch issues; the second is to capture the edge of objects finely without being confused by the surroundings. HRNET has the characteristics of maintaining high-resolution representation by fusing feature information with parallel multi-resolution convolution branches. We adopt HRNET as a backbone and propose to incorporate the Class-Oriented Region Attention Module (CRAM) and Class-Oriented Context Fusion Module (CCFM) to analyze the relationships between classes and patch regions and between classes and local or global pixels, respectively. Thus, the perception capability of the model for the detailed part in the aerial image can be enhanced. We leverage these modules to develop an end-to-end semantic segmentation model for aerial images and validate it on the ISPRS Potsdam and Vaihingen datasets. The experimental results show that our model improves the baseline accuracy and outperforms some commonly used CNN architectures. Full article
(This article belongs to the Section Remote Sensors)
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22 pages, 7943 KiB  
Article
An Unambiguous Synchronization Scheme for GNSS BOC Signals Based on Reconstructed Correlation Function
by Xiyan Sun, Shaojie Song, Yuanfa Ji, Xingli Gan, Suqing Yan and Xizi Jia
Sensors 2021, 21(6), 1982; https://doi.org/10.3390/s21061982 - 11 Mar 2021
Cited by 5 | Viewed by 2588
Abstract
Binary offset carrier (BOC) modulation is a new modulation method that has been gradually applied to the Global Satellite Navigation System (GNSS) in recent years. However, due to the multi-peaks in its auto-correlation function (ACF), it will incur a false lock and generate [...] Read more.
Binary offset carrier (BOC) modulation is a new modulation method that has been gradually applied to the Global Satellite Navigation System (GNSS) in recent years. However, due to the multi-peaks in its auto-correlation function (ACF), it will incur a false lock and generate synchronization ambiguous potentially. In this paper, an unambiguous synchronization method based on a reconstructed correlation function is proposed to solve the ambiguity problem. First, through the shape code vector constructed in this paper, the general cross-correlation function (CCF) expression of the BOC modulated signal will be obtained. Based on the features of the signal correlation function, it is decomposed into a matrix form of trigonometric functions. Then, it generates two local signal waves using a specific method, then the proposed method is implemented to obtain a no-side-peak correlation function by reconstructing the cross-correlation between the received signal and the two local signals. Simulations showed that it fully eliminates the side-peak threat and significantly removes the ambiguity during the synchronization of the BOC signals. This paper also gives the improved structure of acquisition and tracking. The detailed theoretical deduction of detection probability and code tracking error is demonstrated, and the corresponding phase discrimination function is given. In terms of de-blurring ability and detection probability performance, the proposed method outperformed other conventional approaches. The tracking performance was superior to the comparison methods and the phase discrimination curve only had a zero-crossing, which successfully removed the false lock points. In addition, in multipath mitigation, it outperformed the ACF of the BOC signal, and performs as well as the autocorrelation side-peak cancellation technique (ASPeCT) for BOC(kn,n) signals. Full article
(This article belongs to the Section Remote Sensors)
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21 pages, 14628 KiB  
Article
Quantifying Debris Flood Deposits in an Alaskan Fjord Using Multitemporal Digital Elevation Models
by Matthew Balazs, Anupma Prakash and Gabriel Wolken
Sensors 2021, 21(6), 1966; https://doi.org/10.3390/s21061966 - 11 Mar 2021
Cited by 1 | Viewed by 2113
Abstract
Six DEMs over a 10-year period were used to estimate flood-related sedimentation in the Japanese Creek drainage located in Seward, Alaska. We analyze two existing LiDAR DEMs and one GNSS-derived DEM along with three additional DEMs that we generated using differential Global Navigation [...] Read more.
Six DEMs over a 10-year period were used to estimate flood-related sedimentation in the Japanese Creek drainage located in Seward, Alaska. We analyze two existing LiDAR DEMs and one GNSS-derived DEM along with three additional DEMs that we generated using differential Global Navigation Satellite System (dGNSS) and Structure from Motion (SfM) techniques. Uncertainties in each DEM were accounted for, and a DEMs of Difference (DoD) technique was used to quantify the amount and pattern of sediment introduced, redistributed, or exiting the system. Through correlating the changes in sediment budget with rainfall data and flood events, the study demonstrates that the major flood events in 2006 and 2012—the 7th and 5th highest precipitation events on record—resulted in an increased sedimentation in the drainage as a whole. At a minimum the 2006 and 2012 events increased the sediment in the lower reaches by 70,100 and 53,900 cubic meters, respectively. The study shows that the DoD method and using multiple technologies to create DEMs is effective in quantifying the volumetric change and general spatial patterns of sediment redistribution between the acquisition of DEMs. Full article
(This article belongs to the Special Issue Multi-Sensor Techniques for Topographic Mapping)
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14 pages, 4011 KiB  
Article
Feasibility of Using Low-Cost Dual-Frequency GNSS Receivers for Land Surveying
by Natalia Wielgocka, Tomasz Hadas, Adrian Kaczmarek and Grzegorz Marut
Sensors 2021, 21(6), 1956; https://doi.org/10.3390/s21061956 - 11 Mar 2021
Cited by 48 | Viewed by 5474
Abstract
Global Navigation Satellite Systems (GNSS) have revolutionized land surveying, by determining position coordinates with centimeter-level accuracy in real-time or up to sub-millimeter accuracy in post-processing solutions. Although low-cost single-frequency receivers do not meet the accuracy requirements of many surveying applications, multi-frequency hardware is [...] Read more.
Global Navigation Satellite Systems (GNSS) have revolutionized land surveying, by determining position coordinates with centimeter-level accuracy in real-time or up to sub-millimeter accuracy in post-processing solutions. Although low-cost single-frequency receivers do not meet the accuracy requirements of many surveying applications, multi-frequency hardware is expected to overcome the major issues. Therefore, this paper is aimed at investigating the performance of a u-blox ZED-F9P receiver, connected to a u-blox ANN-MB-00-00 antenna, during multiple field experiments. Satisfactory signal acquisition was noticed but it resulted as >7 dB Hz weaker than with a geodetic-grade receiver, especially for low-elevation mask signals. In the static mode, the ambiguity fixing rate reaches 80%, and a horizontal accuracy of few centimeters was achieved during an hour-long session. Similar accuracy was achieved with the Precise Point Positioning (PPP) if a session is extended to at least 2.5 h. Real-Time Kinematic (RTK) and Network RTK measurements achieved a horizontal accuracy better than 5 cm and a sub-decimeter vertical accuracy. If a base station constituted by a low-cost receiver is used, the horizontal accuracy degrades by a factor of two and such a setup may lead to an inaccurate height determination under dynamic surveying conditions, e.g., rotating antenna of the mobile receiver. Full article
(This article belongs to the Section Remote Sensors)
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45 pages, 4487 KiB  
Review
Application of Deep Learning on Millimeter-Wave Radar Signals: A Review
by Fahad Jibrin Abdu, Yixiong Zhang, Maozhong Fu, Yuhan Li and Zhenmiao Deng
Sensors 2021, 21(6), 1951; https://doi.org/10.3390/s21061951 - 10 Mar 2021
Cited by 79 | Viewed by 19032
Abstract
The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data [...] Read more.
The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects. Full article
(This article belongs to the Section Remote Sensors)
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