Remote Sensing

Research

24 pages, 43586 KiB

Open AccessArticle

A Multi-Scale Object Detector Based on Coordinate and Global Information Aggregation for UAV Aerial Images

by Liming Zhou, Zhehao Liu, Hang Zhao, Yan-E Hou, Yang Liu, Xianyu Zuo and Lanxue Dang

Remote Sens. 2023, 15(14), 3468; https://doi.org/10.3390/rs15143468 - 9 Jul 2023

Cited by 4 | Viewed by 1624

Unmanned aerial vehicle (UAV) image object detection has great application value in the military and civilian fields. However, the objects in the captured images from UAVs have problems of large-scale variation, complex backgrounds, and a large proportion of small objects. To resolve these [...] Read more.

Unmanned aerial vehicle (UAV) image object detection has great application value in the military and civilian fields. However, the objects in the captured images from UAVs have problems of large-scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Feature Fusion Module (FFM) is proposed, which can better fuse features by learning the importance of different scale features and improve the representation ability of multi-scale features by reusing feature maps to help models better detect multi-scale objects. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, we tested the CGMDet on VisDrone and UAVDT datasets. The proposed CGMDet improves mAP0.5 by 1.9% on the VisDrone dataset and 3.0% on the UAVDT dataset. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computational Remote Sensing)

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31 pages, 43485 KiB

Open AccessArticle

QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

by David Berga, Pau Gallés, Katalin Takáts, Eva Mohedano, Laura Riordan-Chen, Clara Garcia-Moll, David Vilaseca and Javier Marín

Remote Sens. 2023, 15(9), 2451; https://doi.org/10.3390/rs15092451 - 6 May 2023

Viewed by 1495

Abstract

The latest advances in super-resolution have been tested with general-purpose images such as faces, landscapes and objects, but mainly unused for the task of super-resolving earth observation images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both [...] Read more.

The latest advances in super-resolution have been tested with general-purpose images such as faces, landscapes and objects, but mainly unused for the task of super-resolving earth observation images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both full-reference and no-reference image quality assessment metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict the quality (as a no-reference metric) by training on any property of the image (e.g., its resolution, its distortions, etc.) and also able to optimize SR algorithms for a specific metric objective. This work is part of the implementation of the framework IQUAFLOW, which has been developed for the evaluation of image quality and the detection and classification of objects as well as image compression in EO use cases. We integrated our experimentation and tested our QMRNet algorithm on predicting features such as blur, sharpness, snr, rer and ground sampling distance and obtained validation medRs below 1.0 (out of N = 50) and recall rates above 95%. The overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as a loss for optimizing SR predictions. Due to its simplicity, QMRNet could also be used for other use cases and image domains, as its architecture and data processing is fully scalable. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computational Remote Sensing)

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21 pages, 11503 KiB

Open AccessArticle

A Cross Stage Partial Network with Strengthen Matching Detector for Remote Sensing Object Detection

by Shougang Ren, Zhiruo Fang and Xingjian Gu

Remote Sens. 2023, 15(6), 1574; https://doi.org/10.3390/rs15061574 - 13 Mar 2023

Cited by 1 | Viewed by 1755

Abstract

Remote sensing object detection is a difficult task because it often requires real-time feedback through numerous objects in complex environments. In object detection, Feature Pyramids Networks (FPN) have been widely used for better representations based on a multi-scale problem. However, the multiple level [...] Read more.

Remote sensing object detection is a difficult task because it often requires real-time feedback through numerous objects in complex environments. In object detection, Feature Pyramids Networks (FPN) have been widely used for better representations based on a multi-scale problem. However, the multiple level features cause detectors’ structures to be complex and makes redundant calculations that slow down the detector. This paper uses a single-layer feature to make the detection lightweight and accurate without relying on Feature Pyramid Structures. We proposed a method called the Cross Stage Partial Strengthen Matching Detector (StrMCsDet). The StrMCsDet generates a single-level feature map architecture in the backbone with a cross stage partial network. To provide an alternative way of replacing the traditional feature pyramid, a multi-scale encoder was designed to compensate the receptive field limitation. Additionally, a stronger matching strategy was proposed to make sure that various scale anchors may be equally matched. The StrMCsDet is different from the conventional full pyramid structure and fully exploits the feature map which deals with a multi-scale encoder. Methods achieved both comparable precision and speed for practical applications. Experiments conducted on the DIOR dataset and the NWPU-VHR-10 dataset achieved 65.6 and 73.5 mAP on 1080 Ti, respectively, which can match the performance of state-of-the-art works. Moreover, StrMCsDet requires less computation and achieved 38.5 FPS on the DIOR dataset. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computational Remote Sensing)

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20 pages, 7319 KiB

Open AccessArticle

Comparative Analysis of Machine Learning Algorithms for Soil Erosion Modelling Based on Remotely Sensed Data

by Daniel Fernández, Eromanga Adermann, Marco Pizzolato, Roman Pechenkin, Christina G. Rodríguez and Alireza Taravat

Remote Sens. 2023, 15(2), 482; https://doi.org/10.3390/rs15020482 - 13 Jan 2023

Cited by 7 | Viewed by 3201

Abstract

Recent years have seen an increase in the use of remote-sensing based methods to assess soil erosion, mainly due to the availability of freely accessible satellite data, with successful results on a consistent basis. There would be valuable benefits from applying these techniques [...] Read more.

Recent years have seen an increase in the use of remote-sensing based methods to assess soil erosion, mainly due to the availability of freely accessible satellite data, with successful results on a consistent basis. There would be valuable benefits from applying these techniques to the Arctic areas, where ground local studies are typically difficult to perform due to hardly accessible roads and lands. At the same time, however, the application of remote-sensing methods comes with its own set of challenges when it comes to the peculiar features of the Arctic: short growing periods, winter storms, wind, and frequent cloud and snow cover. In this study we perform a comparative analysis of three commonly used classification algorithms: Support Vector Machine (SVM), Random Forest (RF) and Multilayer Perceptron (MLP), in combination with ground truth samples from regions all over Iceland, provided by Iceland’s Soil Conservation Service department. The process can be automated to predict soil erosion risk for larger, less accessible areas from Sentinel-2 images. The analysis performed on validation data sets supports the effectiveness of both approaches for modeling soil erosion, albeit differences are highlighted. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computational Remote Sensing)

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23 pages, 3947 KiB

Open AccessArticle

TMDiMP: Temporal Memory Guided Discriminative Tracker for UAV Object Tracking

by Zheng Yang, Bing Han, Weiming Chen and Xinbo Gao

Remote Sens. 2022, 14(24), 6351; https://doi.org/10.3390/rs14246351 - 15 Dec 2022

Viewed by 1490

Abstract

Unmanned aerial vehicles (UAVs) have attracted increasing attention in recent years because of their broad range of applications in city security, military reconnaissance, disaster rescue, and so on. As one of the critical algorithms in the field of artificial intelligence, object tracking greatly [...] Read more.

Unmanned aerial vehicles (UAVs) have attracted increasing attention in recent years because of their broad range of applications in city security, military reconnaissance, disaster rescue, and so on. As one of the critical algorithms in the field of artificial intelligence, object tracking greatly improves the working efficiency of UAVs. However, unmanned aerial vehicle (UAV) object tracking still faces many challenges. UAV objects provide limited textures and contours for feature extraction due to their small sizes. Moreover, to capture objects continuously, a UAV camera must constantly move with the object. The above two reasons are usual causes of object-tracking failures. To this end, we propose an end-to-end discriminative tracker called TMDiMP. Inspired by the self-attention mechanism in Transformer, a novel memory-aware attention mechanism is embedded into TMDiMP, which can generate discriminative features of small objects and overcome the object-forgetting problem after camera motion. We also build a UAV object-tracking dataset with various object categories and attributes, named VIPUOTB, which consists of many video sequences collected in urban scenes. Our VIPUOTB is different from other existing datasets in terms of object size, camera motion speed, location distribution, etc. TMDiMP achieves competitive results on our VIPUOTB dataset and three public datasets, UAVDT, UAV123, and VisDrone, compared with state-of-the-art methods, thus demonstrating the effectiveness and robustness of our proposed algorithm. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computational Remote Sensing)

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25 pages, 11633 KiB

Open AccessArticle

Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing

by Andrei Velichko, Maksim Belyaev, Matthias P. Wagner and Alireza Taravat

Remote Sens. 2022, 14(23), 5983; https://doi.org/10.3390/rs14235983 - 25 Nov 2022

Cited by 4 | Viewed by 2147

Abstract

Approximation of entropies of various types using machine learning (ML) regression methods are shown for the first time. The ML models presented in this study define the complexity of the short time series by approximating dissimilar entropy techniques such as Singular value decomposition [...] Read more.

Approximation of entropies of various types using machine learning (ML) regression methods are shown for the first time. The ML models presented in this study define the complexity of the short time series by approximating dissimilar entropy techniques such as Singular value decomposition entropy (SvdEn), Permutation entropy (PermEn), Sample entropy (SampEn) and Neural Network entropy (NNetEn) and their 2D analogies. A new method for calculating SvdEn_2D, PermEn_2D and SampEn_2D for 2D images was tested using the technique of circular kernels. Training and testing datasets on the basis of Sentinel-2 images are presented (two training images and one hundred and ninety-eight testing images). The results of entropy approximation are demonstrated using the example of calculating the 2D entropy of Sentinel-2 images and R² metric evaluation. The applicability of the method for the short time series with a length from N = 5 to N = 113 elements is shown. A tendency for the R² metric to decrease with an increase in the length of the time series was found. For SvdEn entropy, the regression accuracy is R² > 0.99 for N = 5 and R² > 0.82 for N = 113. The best metrics were observed for the ML_SvdEn_2D and ML_NNetEn_2D models. The results of the study can be used for fundamental research of entropy approximations of various types using ML regression, as well as for accelerating entropy calculations in remote sensing. The versatility of the model is shown on a synthetic chaotic time series using Planck map and logistic map. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computational Remote Sensing)

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21 pages, 3041 KiB

Open AccessArticle

Shift Pooling PSPNet: Rethinking PSPNet for Building Extraction in Remote Sensing Images from Entire Local Feature Pooling

by Wei Yuan, Jin Wang and Wenbo Xu

Remote Sens. 2022, 14(19), 4889; https://doi.org/10.3390/rs14194889 - 30 Sep 2022

Cited by 19 | Viewed by 2636

Abstract

Building extraction by deep learning from remote sensing images is currently a research hotspot. PSPNet is one of the classic semantic segmentation models and is currently adopted by many applications. Moreover, PSPNet can use not only CNN-based networks but also transformer-based networks as [...] Read more.

Building extraction by deep learning from remote sensing images is currently a research hotspot. PSPNet is one of the classic semantic segmentation models and is currently adopted by many applications. Moreover, PSPNet can use not only CNN-based networks but also transformer-based networks as backbones; therefore, PSPNet also has high value in the transformer era. The core of PSPNet is the pyramid pooling module, which gives PSPNet the ability to capture the local features of different scales. However, the pyramid pooling module also has obvious shortcomings. The grid is fixed, and the pixels close to the edge of the grid cannot obtain the entire local features. To address this issue, an improved PSPNet network architecture named shift pooling PSPNet is proposed, which uses a module called shift pyramid pooling to replace the original pyramid pooling module, so that the pixels at the edge of the grid can also obtain the entire local features. Shift pooling is not only useful for PSPNet but also in any network that uses a fixed grid for downsampling to increase the receptive field and save computing, such as ResNet. A dense connection was adopted in decoding, and upsampling was gradually carried out. With two open datasets, the improved PSPNet, PSPNet, and some classic image segmentation models were used for comparative experiments. The results show that our method is the best according to the evaluation metrics, and the predicted image is closer to the label. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computational Remote Sensing)

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25 pages, 18409 KiB

Open AccessArticle

NNetEn_2D: Two-Dimensional Neural Network Entropy in Remote Sensing Imagery and Geophysical Mapping

by Andrei Velichko, Matthias P. Wagner, Alireza Taravat, Bruce Hobbs and Alison Ord

Remote Sens. 2022, 14(9), 2166; https://doi.org/10.3390/rs14092166 - 30 Apr 2022

Cited by 6 | Viewed by 2206

Abstract

Measuring the predictability and complexity of 2D data (image) series using entropy is an essential tool for evaluation of systems’ irregularity and complexity in remote sensing and geophysical mapping. However, the existing methods have some drawbacks related to their strong dependence on method [...] Read more.

Measuring the predictability and complexity of 2D data (image) series using entropy is an essential tool for evaluation of systems’ irregularity and complexity in remote sensing and geophysical mapping. However, the existing methods have some drawbacks related to their strong dependence on method parameters and image rotation. To overcome these difficulties, this study proposes a new method for estimating two-dimensional neural network entropy (NNetEn_2D) for evaluating the regularity or predictability of images using the LogNNet neural network model. The method is based on an algorithm for converting a 2D kernel into a 1D data series followed by NNetEn_2D calculation. An artificial test image was created for the study. We demonstrate the advantage of using circular instead of square kernels through comparison of the invariance of the NNetEn_2D distribution after image rotation. Highest robustness was observed for circular kernels with a radius of R = 5 and R = 6 pixels, with a NNetEn_2D calculation error of no more than 10%, comparable to the distortion of the initial 2D data. The NNetEn_2D entropy calculation method has two main geometric parameters (kernel radius and its displacement step), as well as two neural network hyperparameters (number of training epochs and one of six reservoir filling techniques). We evaluated our method on both remote sensing and geophysical mapping images. Remote sensing imagery (Sentinel-2) shows that brightness of the image does not affect results, which helps keep a rather consistent appearance of entropy maps over time without saturation effects being observed. Surfaces with little texture, such as water bodies, have low NNetEn_2D values, while urban areas have consistently high values. Application to geophysical mapping of rocks to the northwest of southwest Australia is characterized by low to medium entropy and highlights aspects of the geology. These results indicate the success of NNetEn_2D in providing meaningful entropy information for 2D in remote sensing and geophysical applications. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computational Remote Sensing)

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