Remote Sensing

Research

21 pages, 9002 KiB

Open AccessArticle

A New Method for Continuous Track Monitoring in Regions of Differential Land Subsidence Rate Using the Integration of PS-InSAR and SBAS-InSAR

by Peng Zhang, Xiaqing Qian, Shuangfeng Guo, Bikai Wang, Jin Xia and Xiaohui Zheng

Remote Sens. 2023, 15(13), 3298; https://doi.org/10.3390/rs15133298 - 27 Jun 2023

Cited by 3 | Viewed by 2236

Abstract

It is difficult for single time-series Interferometric Synthetic Aperture Radar (InSAR) processing to guarantee the accuracy and efficiency of continuous track monitoring in regions of differential subsidence. This paper proposes a new method, integrating the Persistent Scatterer InSAR (PS-InSAR) with high precision and [...] Read more.

It is difficult for single time-series Interferometric Synthetic Aperture Radar (InSAR) processing to guarantee the accuracy and efficiency of continuous track monitoring in regions of differential subsidence. This paper proposes a new method, integrating the Persistent Scatterer InSAR (PS-InSAR) with high precision and the Small Baseline Subset InSAR (SBAS-InSAR) with high efficiency for continuous track monitoring in regions of differential land subsidence rates. Based on PS-InSAR processing, the Iterative Self-Organizing Data Analysis Techniques (ISODATA) algorithm is adopted to search the boundary of differential subsidence between slow and fast subsidence rates. The SBAS-InSAR processing with high frequency is used to continuously track and monitor the regions with fast subsidence rates incorporating original data and newly added data into small data sets from time to time according to SAR data updating, the monitoring results of which are obtained from the weighted average of the added results of SBAS-InSAR processing and the original results of PS-InSAR processing. The impact of SAR data updating on the slow subsidence rate region is so weak that it is not necessary to simultaneously update the corresponding monitoring results to improve global efficiency. If the slow subsidence rates region must be remeasured in relation to its previous subsidence, or the proportion of new data capacity alters compared with the original data set, PS-InSAR processing is used to analyze the whole monitoring region again using the complete data set. A case study performed on the west region of the Qinhuai River in Nanjing, China, indicates that the density of monitoring points in the fast-subsidence region is greatly improved, increasing from 711 points/km

^{2}

to 2760 points/km

^{2}

—an increase of 288.2%. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Figure 1

20 pages, 11782 KiB

Open AccessArticle

How to Systematically Evaluate the Greenspace Exposure of Residential Communities? A 3-D Novel Perspective Using UAV Photogrammetry

by Tianyu Xia, Bing Zhao, Zheng Xian and Jinguang Zhang

Remote Sens. 2023, 15(6), 1543; https://doi.org/10.3390/rs15061543 - 11 Mar 2023

Cited by 5 | Viewed by 2606

Abstract

The quantity and quality of green space (GS) exposure play an important role in urban residents’ physical and psychological health. However, the current framework for assessing GS quality is primarily based on 2-D remote sensing data and 2.5-D street-view images. Few studies have [...] Read more.

The quantity and quality of green space (GS) exposure play an important role in urban residents’ physical and psychological health. However, the current framework for assessing GS quality is primarily based on 2-D remote sensing data and 2.5-D street-view images. Few studies have comprehensively evaluated residential community GSs from an overall 3-D perspective. This study proposes a novel systematic framework for evaluating the quantity and quality of residential GSs based on the generation of a high-resolution 3-D point cloud using Unmanned Aerial Vehicle (UAV)-digital aerial photogrammetry (DAP). Nine indices were proposed: green volume ratio, floor green volume index, green groups diversity index, vegetation diversity index, greenspace fragmentation, average vegetation colour distance, vegetation colour diversity, activity areas ratio, and green cohesion index of activity site. These metrics were calculated using the classified point clouds from four typical Chinese residential communities with different residential greenery types and population densities. The results showed that our method could quantitatively identify the differences in residential GS exposure within urban residential communities. For example, a residential community with a large plant distribution and rich greenery variations had higher greenspace volume ratio and vegetation diversity index values. Our findings suggest that this novel framework, employing cost-effective UAV-DAP, can clearly describe different GS attributes and characteristics, aiding decision-makers and urban planners in comprehensively implementing GS interventions to improve the residents’ quality of life. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Figure 1

20 pages, 5237 KiB

Open AccessArticle

Detection and Evaluation of Flood Inundation Using CYGNSS Data during Extreme Precipitation in 2022 in Guangdong Province, China

by Haohan Wei, Tongning Yu, Jinsheng Tu and Fuyang Ke

Remote Sens. 2023, 15(2), 297; https://doi.org/10.3390/rs15020297 - 4 Jan 2023

Cited by 10 | Viewed by 2930

Abstract

Floods are severe natural disasters that are harmful and frequently occur across the world. From May to July 2022, the strongest, broadest, and longest rainfall event in recent years occurred in Guangdong Province, China. The flooding caused by continuous precipitation and a typhoon [...] Read more.

Floods are severe natural disasters that are harmful and frequently occur across the world. From May to July 2022, the strongest, broadest, and longest rainfall event in recent years occurred in Guangdong Province, China. The flooding caused by continuous precipitation and a typhoon resulted in severe losses to local people and property. During flood events, there is an urgent need for timely and detailed flood inundation mapping for areas that have been severely affected. However, current satellite missions cannot provide sufficient information at a high enough spatio-temporal resolution for flooding applications. In contrast, spaceborne Global Navigation Satellite System reflectometry technology can be used to observe the Earth’s surface at a high spatio-temporal resolution without being affected by clouds or surface vegetation, providing a feasible scheme for flood disaster research. In this study, Cyclone Global Navigation Satellite System (CYGNSS) L1 science data were processed to obtain the change in the delay-Doppler map and surface reflectivity (SR) during the flood event. Then, a flood inundation map of the extreme precipitation was drawn using the threshold method based on the CYGNSS SR. Additionally, the flooded areas that were calculated based on the soil moisture from the Soil Moisture Active Passive (SMAP) data were used as a reference. Furthermore, the daily Dry Wet Abrupt Alternation Index (DWAAI) was used to identify the occurrence of the flood events. The results showed good agreement between the flood inundation that was derived from the CYGNSS SR and SMAP soil moisture. Moreover, compared with the SMAP results, the CYGNSS SR can provide the daily flood inundation with higher accuracy due to its high spatio-temporal resolution. Furthermore, the DWAAI can identify the transformation from droughts to floods in a relatively short period. Consequently, the distributions of and variations in flood inundation under extreme weather conditions can be identified on a daily scale with good accuracy using the CYGNSS data. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Figure 1

15 pages, 8074 KiB

Open AccessArticle

DCFusion: Dual-Headed Fusion Strategy and Contextual Information Awareness for Infrared and Visible Remote Sensing Image

by Qin Pu, Abdellah Chehri, Gwanggil Jeon, Lei Zhang and Xiaomin Yang

Remote Sens. 2023, 15(1), 144; https://doi.org/10.3390/rs15010144 - 27 Dec 2022

Cited by 2 | Viewed by 2080

Abstract

In remote sensing, the fusion of infrared and visible images is one of the common means of data processing. Its aim is to synthesize one fused image with abundant common and differential information from the source images. At present, the fusion methods based [...] Read more.

In remote sensing, the fusion of infrared and visible images is one of the common means of data processing. Its aim is to synthesize one fused image with abundant common and differential information from the source images. At present, the fusion methods based on deep learning are widely employed in this work. However, the existing fusion network with deep learning fails to effectively integrate common and differential information for source images. To alleviate the problem, we propose a dual-head fusion strategy and contextual information awareness fusion network (DCFusion) to preserve more meaningful information from source images. Firstly, we extract multi-scale features for the source images with multiple convolution and pooling layers. Then, we propose a dual-headed fusion strategy (DHFS) to fuse different modal features from the encoder. The DHFS can effectively preserve common and differential information for different modal features. Finally, we propose a contextual information awareness module (CIAM) to reconstruct the fused image. The CIAM can adequately exchange information from different scale features and improve fusion performance. Furthermore, the whole network was tested on MSRS and TNO datasets. The results of extensive experiments prove that our proposed network achieves good performance in target maintenance and texture preservation for fusion images. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Figure 1

21 pages, 8936 KiB

Open AccessArticle

Integrating Spatial Heterogeneity to Identify the Urban Fringe Area Based on NPP/VIIRS Nighttime Light Data and Dual Spatial Clustering

by Jie Zhu, Ziqi Lang, Jing Yang, Meihui Wang, Jiazhu Zheng and Jiaming Na

Remote Sens. 2022, 14(23), 6126; https://doi.org/10.3390/rs14236126 - 2 Dec 2022

Cited by 10 | Viewed by 1879

Abstract

The precise recognition of urban fringes is vital to monitor urban sprawl and map urban management planning. The spatial clustering method is a prevalent way to identify urban fringes due to its objectivity and convenience. However, previous studies had problems with ignoring spatial [...] Read more.

The precise recognition of urban fringes is vital to monitor urban sprawl and map urban management planning. The spatial clustering method is a prevalent way to identify urban fringes due to its objectivity and convenience. However, previous studies had problems with ignoring spatial heterogeneity, which could overestimate or underestimate the recognition results. Nighttime light can reflect the transitional urban–rural regions’ regional spatial characteristics and can be used to identify urban fringes. Accordingly, a new model has been established for urban fringe identification by combining spatial continuous wavelet transform (SCWT) and dual spatial clustering. Then, Nanjing City, China, as a case study, is employed to validate the model through the NPP/VIIRS nighttime light data. The identification of mutated points across the urban–rural gradient is conducted by utilizing the SCWT. By using dual spatial clustering in the urban fringe identification, it transmits the mutation points’ spatial patterns to the homogeneous spatially neighboring clusters effectively, which measures the similarity between mutation points regarding spatial and attribute domains. A comparison of the identified results by various spatial clustering approaches revealed that our method could be more suitable for the impacts of mutation points’ local spatial patterns on different density values over the whole density surface, thus leading to more accurate spatial boundaries featured by differentiating actual differences of mutation points between adjacent clusters. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Graphical abstract

25 pages, 16164 KiB

Open AccessArticle

Evaluation of Mangrove Wetlands Protection Patterns in the Guangdong–Hong Kong–Macao Greater Bay Area Using Time-Series Landsat Imageries

by Tingting He, Yingchun Fu, Hu Ding, Weiping Zheng, Xiaohui Huang, Runhao Li and Shuting Wu

Remote Sens. 2022, 14(23), 6026; https://doi.org/10.3390/rs14236026 - 28 Nov 2022

Cited by 7 | Viewed by 2727

Abstract

The protection of mangroves through nature reserves has been demonstrated to be effective. There were many studies evaluating the mangrove protection effect. However, the evaluation of mangrove growth quality with positive or negative growth trends, as well as restoration potential against disturbance in [...] Read more.

The protection of mangroves through nature reserves has been demonstrated to be effective. There were many studies evaluating the mangrove protection effect. However, the evaluation of mangrove growth quality with positive or negative growth trends, as well as restoration potential against disturbance in nature reserves, is still lacking. Thus, this study proposed a hierarchical evaluation framework for mangrove protection in nature reserves, which takes long-term metrics at three levels of loss and gain areas, patch pattern dynamics, and pixel growth trends into account. The continuous change detection and classification (CCDC) was utilized to identify the change condition of mangroves in six nature reserves of the Guangdong–Hong Kong–Macao Greater Bay Area. The Entropy Weight Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was utilized for scores evaluation of protection effort comparison from 2000 to 2020. The study results had the following three main findings. Firstly, the mangrove forest area increased by about 294.66 ha in four reserves and slightly decreased by about 58.86 ha in two. Most reserves showed an improved patches intact pattern and more positive growth trends. Secondly, the establishment of nature reserves and afforestation were the main causes of mangrove area gain. Until 2010, aquaculture, agriculture, and urban development were the biggest threats to mangroves. Finally, the protection of the reserves was successful in the early decades, but the general evaluation scores showed a decline in recent years once we considered the growth trends for quality. The proposed hierarchical evaluation methods provide a new sight to research the impacts of abrupt change and protection resilience status of the gradual restoration of nature reserves. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Figure 1

15 pages, 3040 KiB

Open AccessArticle

Evaluation of LiDAR-Derived Features Relevance and Training Data Minimization for 3D Point Cloud Classification

by Salem Morsy and Ahmed Shaker

Remote Sens. 2022, 14(23), 5934; https://doi.org/10.3390/rs14235934 - 23 Nov 2022

Cited by 9 | Viewed by 2247

Abstract

Terrestrial laser scanning (TLS) is a leading technology in data acquisition for building information modeling (BIM) applications due to its rapid, direct, and accurate scanning of different objects with high point density. Three-dimensional point cloud classification is essential step for Scan-to-BIM applications that [...] Read more.

Terrestrial laser scanning (TLS) is a leading technology in data acquisition for building information modeling (BIM) applications due to its rapid, direct, and accurate scanning of different objects with high point density. Three-dimensional point cloud classification is essential step for Scan-to-BIM applications that requires high accuracy classification methods, running at reasonable processing time. The classification process is divided into three main steps: neighborhood definition, LiDAR-derived features extraction, and machine learning algorithms being applied to label each LiDAR point. However, the extraction of LiDAR-derived features and training data are time consuming. This research aims to minimize the training data, assess the relevance of sixteen LiDAR-derived geometric features, and select the most contributing features to the classification process. A pointwise classification method based on random forests is applied on the 3D point cloud of a university campus building collected by a TLS system. The results demonstrated that the normalized height feature, which represented the absolute height above ground, was the most significant feature in the classification process with overall accuracy more than 99%. The training data were minimized to about 10% of the whole dataset with achieving the same level of accuracy. The findings of this paper open doors for BIM-related applications such as city digital twins, operation and maintenance of existing structures, and structural health monitoring. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Graphical abstract

20 pages, 4441 KiB

Open AccessArticle

SATNet: A Spatial Attention Based Network for Hyperspectral Image Classification

by Qingqing Hong, Xinyi Zhong, Weitong Chen, Zhenghua Zhang, Bin Li, Hao Sun, Tianbao Yang and Changwei Tan

Remote Sens. 2022, 14(22), 5902; https://doi.org/10.3390/rs14225902 - 21 Nov 2022

Cited by 9 | Viewed by 2463

Abstract

In order to categorize feature classes by capturing subtle differences, hyperspectral images (HSIs) have been extensively used due to the rich spectral-spatial information. The 3D convolution-based neural networks (3DCNNs) have been widely used in HSI classification because of their powerful feature extraction capability. [...] Read more.

In order to categorize feature classes by capturing subtle differences, hyperspectral images (HSIs) have been extensively used due to the rich spectral-spatial information. The 3D convolution-based neural networks (3DCNNs) have been widely used in HSI classification because of their powerful feature extraction capability. However, the 3DCNN-based HSI classification approach could only extract local features, and the feature maps it produces include a lot of spatial information redundancy, which lowers the classification accuracy. To solve the above problems, we proposed a spatial attention network (SATNet) by combining 3D OctConv and ViT. Firstly, 3D OctConv divided the feature maps into high-frequency maps and low-frequency maps to reduce spatial information redundancy. Secondly, the ViT model was used to obtain global features and effectively combine local-global features for classification. To verify the effectiveness of the method in the paper, a comparison with various mainstream methods on three publicly available datasets was performed, and the results showed the superiority of the proposed method in terms of classification evaluation performance. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Figure 1

24 pages, 7365 KiB

Open AccessArticle

A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR Data

by Xiao Li, Fang Qiu, Fan Shi and Yunwei Tang

Remote Sens. 2022, 14(22), 5892; https://doi.org/10.3390/rs14225892 - 21 Nov 2022

Cited by 7 | Viewed by 2208

Abstract

Automatically generating a building footprint from an airborne LiDAR point cloud is an active research topic because of its widespread usage in numerous applications. This paper presents an efficient and automated workflow for generating building footprints from pre-classified LiDAR data. In this workflow, [...] Read more.

Automatically generating a building footprint from an airborne LiDAR point cloud is an active research topic because of its widespread usage in numerous applications. This paper presents an efficient and automated workflow for generating building footprints from pre-classified LiDAR data. In this workflow, LiDAR points that belong to the building category are first segmented into multiple clusters by applying the grid-based DBSCAN clustering algorithm. Each cluster contains the points of an individual building. Then, the outermost points of each building are extracted, on which the recursive convex hull algorithm is applied to generate the initial outline of each building. Since LiDAR points are irregularly distributed, the initial building outline contains irregular zig-zag shapes. In order to achieve a regularized building footprint that is close to the true building boundary, a signal-based regularization algorithm is developed. The initial outline is first transformed into a signal, which can reveal the wholistic geometric structure of the building outline after applying a denoising procedure. By analyzing the denoised signal, the locations of corners are identified, and the regularized building footprint is generated. The performance of the proposed workflow is tested and evaluated using two datasets that have different point densities and building types. The qualitative assessment reveals that the proposed workflow has a satisfying performance in generating building footprints even for building with complex structures. The quantitative assessment compares the performance of signal-based regularization with existing regularization methods using the 149 buildings contained in the test dataset. The experimental result shows the proposed method has achieved superior results based on a number of commonly used accuracy metrics. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Figure 1

17 pages, 7337 KiB

Open AccessArticle

Improving the Performance of Automated Rooftop Extraction through Geospatial Stratified and Optimized Sampling

by Zhuo Sun, Zhixin Zhang, Min Chen, Zhen Qian, Min Cao and Yongning Wen

Remote Sens. 2022, 14(19), 4961; https://doi.org/10.3390/rs14194961 - 5 Oct 2022

Cited by 5 | Viewed by 2111

Abstract

Accurate and timely access to building rooftop information is very important for urban management. The era of big data brings new opportunities for rooftop extraction based on deep learning and high-resolution satellite imagery. However, collecting representative datasets from such big data to train [...] Read more.

Accurate and timely access to building rooftop information is very important for urban management. The era of big data brings new opportunities for rooftop extraction based on deep learning and high-resolution satellite imagery. However, collecting representative datasets from such big data to train deep learning models efficiently is an essential problem that still needs to be explored. In this study, geospatial stratified and optimized sampling (GSOS) based on geographical priori information and optimization of sample spatial location distribution is proposed to acquire representative samples. Specifically, the study area is stratified based on land cover to divide the rooftop-dense stratum and the rooftop-sparse stratum. Within each stratum, an equal amount of samples is collected and their spatial locations are optimized. To evaluate the effectiveness of the proposed strategy, several qualitive and quantitative experiments are conducted. As a result, compared with other common sampling approaches (e.g., random sampling, stratified random sampling, and optimized sampling), GSOS is superior in terms of the abundance and types of collected samples. Furthermore, two quantitative metrics, the F₁-score and Intersection over Union (IoU), are reported for rooftop extraction based on deep learning methods and different sampling methods, in which the results based on GSOS are on average 9.88% and 13.20% higher than those based on the other sampling methods, respectively. Moreover, the proposed sampling strategy is able to obtain representative training samples for the task of building rooftop extractions and may serve as a viable method to alleviate the labour-intensive problem in the construction of rooftop benchmark datasets. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Figure 1

17 pages, 2204 KiB

Open AccessArticle

Multi-Source Time Series Remote Sensing Feature Selection and Urban Forest Extraction Based on Improved Artificial Bee Colony

by Jin Yan, Yuanyuan Chen, Jiazhu Zheng, Lin Guo, Siqi Zheng and Rongchun Zhang

Remote Sens. 2022, 14(19), 4859; https://doi.org/10.3390/rs14194859 - 29 Sep 2022

Cited by 5 | Viewed by 2263

Abstract

Urban forests maintain the ecological balance of cities and are significant in promoting the sustainable development of cities. Therefore, using advanced remote sensing technology to accurately extract forest green space in the city and monitor its change in real-time is very important. Taking [...] Read more.

Urban forests maintain the ecological balance of cities and are significant in promoting the sustainable development of cities. Therefore, using advanced remote sensing technology to accurately extract forest green space in the city and monitor its change in real-time is very important. Taking Nanjing as the study area, this research extracted 55 vegetation phenological features from Sentinel-2A time series images and formed a feature set containing 81 parameters together with 26 features, including polarimetric- and texture-related information extracted from dual-polarization Sentinel-1A data. On the basis of the improved ABC (ABC-LIBSVM) feature selection method, the optimal feature subset was selected, and the forest coverage areas in the study area were accurately described. To verify the feasibility of the improved feature selection method and explore the potential for the development of multi-source time series remote sensing for urban forest feature extraction, this paper also used the random forest classification model to classify four different feature sets. The results revealed that the classification accuracy based on the feature set obtained by the ABC-LIBSVM algorithm was the highest, with an overall accuracy of 86.80% and a kappa coefficient of 0.8145. The producer accuracy and user accuracy of the urban forest were 93.21% and 82.45%, respectively. Furthermore, by combining the multi-source time series Sentinel-2A optical images with Sentinel-1A dual-polarization SAR images, urban forests can be distinguished from the perspective of phenology, and polarimetric- and texture-related features can contribute to the accurate identification of forests. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Graphical abstract

20 pages, 7980 KiB

Open AccessArticle

New Era for Geo-Parsing to Obtain Actual Locations: A Novel Toponym Correction Method Based on Remote Sensing Images

by Shu Wang, Xinrong Yan, Yunqiang Zhu, Jia Song, Kai Sun, Weirong Li, Lei Hu, Yanmin Qi and Huiyao Xu

Remote Sens. 2022, 14(19), 4725; https://doi.org/10.3390/rs14194725 - 21 Sep 2022

Cited by 1 | Viewed by 1861

Abstract

Geo-parsing, one of the key components of geographical information retrieval, is a process to recognize and geo-locate toponyms mentioned in texts. Such a process can obtain locations contained in toponyms successfully with consistent updating of neural network models and multiple contextual features. The [...] Read more.

Geo-parsing, one of the key components of geographical information retrieval, is a process to recognize and geo-locate toponyms mentioned in texts. Such a process can obtain locations contained in toponyms successfully with consistent updating of neural network models and multiple contextual features. The significant offset distance between the geo-parsed locations and the actual occurrence locations still remains. This is because the geo-parsed locations sourced from toponyms in texts always point to the centers of cities, counties, or towns, and cannot directly represent the actual occurrence locations such as factories, farms, and activity areas. Consequently, The significant offset distances between the geo-parsed locations and the actual occurrence locations limit text mining applications in micro-scale geographic discoveries. This research aims at decreasing offset distances of geo-parsed locations by proposing a novel Toponym Correction Method based on satellite Remote Sensing Images (TC-RSI). The TC-RSI method uses satellite remote sensing images to provide extra detailed spatial information that can be associated with the sentence toponym by corresponding attributes. The TC-RSI method was validated in a case study of the forest ecological pattern dataset of An’hui province from visual, statistical, and robustness assessments. The correction results show that the TC-RSI method dramatically decreases the offset distances from about 50 km to about 1 km and promotes geographical discoveries on smaller scales. A series of analyses indicated that the TC-RSI is a valid, effective, and promising method to improve the accuracy of geo-parsed locations, which allows text mining to find more accurate geographical discoveries with lower offset distances. Moreover, toponym correction promotes the use of more diverse spatial data sources, such as Lidar, domain gazetteers, Wikimedia, and streetscapes, which are expected to usher in a new era of geo-parsing with toponym corrections. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Graphical abstract

18 pages, 13890 KiB

Open AccessArticle

Identifying Urban Functional Regions from High-Resolution Satellite Images Using a Context-Aware Segmentation Network

by Wufan Zhao, Mengmeng Li, Cai Wu, Wen Zhou and Guozhong Chu

Remote Sens. 2022, 14(16), 3996; https://doi.org/10.3390/rs14163996 - 17 Aug 2022

Cited by 10 | Viewed by 3147

Abstract

The automatic identification of urban functional regions (UFRs) is crucial for urban planning and management. A key issue involved in URF classification is to properly determine the basic functional units, for which popular practices are usually based upon existing land use boundaries or [...] Read more.

The automatic identification of urban functional regions (UFRs) is crucial for urban planning and management. A key issue involved in URF classification is to properly determine the basic functional units, for which popular practices are usually based upon existing land use boundaries or road networks. Such practices suffer from the unavailability of existing datasets, leading to difficulty in large-scale mapping. To deal with this problem, this paper presents a method to automatically obtain functional units for URF classification using high-resolution remote sensing images. We develop a context-aware segmentation network to simultaneously extract buildings and road networks from remote sensing images. The extracted road networks are used for partitioning functional units, upon which five main building types are distinguished considering building height, morphology, and geometry. Finally, the UFRs are classified according to the distribution of building types. We conducted experiments using a GaoFen-2 satellite image with a spatial resolution of 0.8 m acquired in Fuzhou, China. Experimental results showed that the proposed segmentation network performed better than other convolutional neural network segmentation methods (i.e., PSPNet, Deeplabv3+, DANet, and JointNet), with an increase of F1-score up to

1.37 %

and

1.19 %

for road and building extraction, respectively. Results also showed that the residential regions, accounting for most of the urban areas, identified by the proposed method had a user accuracy of

94 %

, implying the promise of the proposed method for deriving the spatial units and the types of urban functional regions. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Figure 1

20 pages, 12761 KiB

Open AccessArticle

Land Subsidence Monitoring Method in Regions of Variable Radar Reflection Characteristics by Integrating PS-InSAR and SBAS-InSAR Techniques

by Peng Zhang, Zihao Guo, Shuangfeng Guo and Jin Xia

Remote Sens. 2022, 14(14), 3265; https://doi.org/10.3390/rs14143265 - 6 Jul 2022

Cited by 30 | Viewed by 4602

Abstract

In the InSAR solution, the uneven distribution of permanent scatterer candidates (PSCs) or slowly decoherent filtering phase (SDFP) pixel density in a region of variable radar reflection feature can cause local low accuracy in single interferometry. PSCs with higher-order coherence in Permanent Scatter [...] Read more.

In the InSAR solution, the uneven distribution of permanent scatterer candidates (PSCs) or slowly decoherent filtering phase (SDFP) pixel density in a region of variable radar reflection feature can cause local low accuracy in single interferometry. PSCs with higher-order coherence in Permanent Scatter InSAR (PS-InSAR) are generally distributed in those point targets of urban built-up areas, and SDFP pixels in Small Baseline Subset InSAR (SBAS-InSAR) are generally distributed in those distributed targets of countryside vegetation areas. According to the respective reliability of PS-InSAR and SBAS-InSAR for different radar reflection features, a new land subsidence monitoring method is proposed, which combines PS-SBAS InSAR by data fusion of different interferometry in different radar reflection regions. Density-based spatial clustering of applications with noise (DBSCAN) clustering analysis is carried out on the density of PSCs with higher-order coherence in PS-InSAR processing to zone the region of variable radar reflection features for acquiring the boundary of data fusion. The vector monitoring data of PS-InSAR is retained in the dense region of PSCs with higher-order coherence, and the vector monitoring data of SBAS-InSAR is used in the sparse region of PSCs with higher-order coherence. The vertical displacements from PS-InSAR and SBAS-InSAR are integrated to obtain the optimal land subsidence. The verification case of 38 SAR images acquired by the Sentinel-1A in Suzhou city indicates that the proposed method can automatically choose a matched interferometry technique according to the variability of radar reflection features in the region and improve the accuracy of using a single interferometry method. The integrated method of the combined field is more representative of overall subsidence characteristics than the PS-InSAR-only or SBAS-InSAR-only results, and it is better suited for the assessment of the impact of land subsidence over the study area. The research results of this paper can provide a useful comprehensive reference for city planning and help decrease land subsidence in Suzhou. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Figure 1

25 pages, 8824 KiB

Open AccessArticle

DGS-SLAM: A Fast and Robust RGBD SLAM in Dynamic Environments Combined by Geometric and Semantic Information

by Li Yan, Xiao Hu, Leyang Zhao, Yu Chen, Pengcheng Wei and Hong Xie

Remote Sens. 2022, 14(3), 795; https://doi.org/10.3390/rs14030795 - 8 Feb 2022

Cited by 41 | Viewed by 5906

Abstract

Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to accomplish fully autonomous movement and exploration in unknown environments. At present, many impressive VSLAM systems have emerged, but most of them rely on the static world assumption, which limits their application [...] Read more.

Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to accomplish fully autonomous movement and exploration in unknown environments. At present, many impressive VSLAM systems have emerged, but most of them rely on the static world assumption, which limits their application in real dynamic scenarios. To improve the robustness and efficiency of the system in dynamic environments, this paper proposes a dynamic RGBD SLAM based on a combination of geometric and semantic information (DGS-SLAM). First, a dynamic object detection module based on the multinomial residual model is proposed, which executes the motion segmentation of the scene by combining the motion residual information of adjacent frames and the potential motion information of the semantic segmentation module. Second, a camera pose tracking strategy using feature point classification results is designed to achieve robust system tracking. Finally, according to the results of dynamic segmentation and camera tracking, a semantic segmentation module based on a semantic frame selection strategy is designed for extracting potential moving targets in the scene. Extensive evaluation in public TUM and Bonn datasets demonstrates that DGS-SLAM has higher robustness and speed than state-of-the-art dynamic RGB-D SLAM systems in dynamic scenes. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Graphical abstract

17 pages, 4432 KiB

Open AccessArticle

Fine-Scale Improved Carbon Bookkeeping Model Using Landsat Time Series for Subtropical Forest, Southern China

by Xinyu Wang, Runhao Li, Hu Ding and Yingchun Fu

Remote Sens. 2022, 14(3), 753; https://doi.org/10.3390/rs14030753 - 6 Feb 2022

Cited by 5 | Viewed by 2776

Abstract

Subtropical forests easily suffer anthropogenic disturbance, including deforestation and reforestation management, which both highly affect the carbon pools. This study proposes spatial-temporal tracking of the carbon density dynamics to improve bookkeeping in the carbon model and applied to subtropical forest activities in Guangzhou, [...] Read more.

Subtropical forests easily suffer anthropogenic disturbance, including deforestation and reforestation management, which both highly affect the carbon pools. This study proposes spatial-temporal tracking of the carbon density dynamics to improve bookkeeping in the carbon model and applied to subtropical forest activities in Guangzhou, southern China, during the period of 1995 to 2014. Based on the overall accuracy of 87.5% ± 1.7% for forest change products using Landsat time series (LTS), we found that this is a typical period of deforestation conversion to reforestation activity accompanied with urbanization. Additionally, linear regression, random forest regression and allometric growth fitting were proposed by using forest field plots to obtain reliable per-pixel carbon density estimations. The cross-validation (CV) of random forest with LTS-derived parameters reached the highest accuracy of R² and RMSE of 0.763 and 7.499 Mg ha⁻¹. The RMES of the density estimation ranged between 78 and 84% of the mean observed biomass in the study area, which outperformed previous studies. Over the 20-year period, the study results showed that the explicit carbon emissions were (6.82 ± 0.26) × 10⁴ Mg C yr⁻¹ from deforestation; emissions increased to (1.02 ± 0.04) × 10⁵ Mg C yr⁻¹ given the implicit carbon not yet released to the atmosphere in the form of decomposing slash and wood products. In addition, a carbon uptake of about 1.91 ± 0.73 × 10⁵ Mg C yr⁻¹, presented as the net carbon pool. Based on the continuous detection capability, biennial reforestation activity has increased carbon density by a growth rate of 1.55 Mg ha⁻¹, and the emission factors can be identified with LTS-derived parameters. In general, the study realizes the spatiotemporal improvement of carbon density and flux dynamics tracking, including the abrupt and graduate change based on fine-scale forest activity. It can provide more comprehensive and detailed feedback on the carbon source and sink change process of forest activities and disturbances. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Graphical abstract

19 pages, 3646 KiB

Open AccessArticle

Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models

by Peilong Ma, Fei Tao, Lina Gao, Shaijie Leng, Ke Yang and Tong Zhou

Remote Sens. 2022, 14(3), 599; https://doi.org/10.3390/rs14030599 - 26 Jan 2022

Cited by 17 | Viewed by 3638

Abstract

Due to the country’s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is [...] Read more.

Due to the country’s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R². The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R² of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city. Full article

(This article belongs to the Special Issue Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (17 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI