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SAR in Big Data Era II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 31054

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Special Issue Editors

Prof. Dr. Chao Wang

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Guest Editor

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Interests: SAR image understanding; PolSAR and InSAR applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Alberto Moreira

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DLR, Microwaves and Radar Institute, Muenchener Str. 20, D-82234 Wessling, Germany
Interests: SAR technology; SAR missions; SAR applications
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Prof. Dr. Mihai Datcu

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Guest Editor

1. Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR) Oberpfaffenhofen, D-82234 Wessling, Germany
2. Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology (ETTI), University Politehnica of Bucharest UPB, 061071 Bucharest, Romania
Interests: signal processing; information theory; big data mining; VHR SAR; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Fan Wu

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Guest Editor

Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: SAR applications

Dr. Ruya Xiao

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Guest Editor

School of Earth Sciences and Engineering, Hohai University, 8th Focheng West Road, Nanjing 211100, China
Interests: InSAR; time series analysis; InSAR atmospheric correction; deformation monitoring; geodesy; GNSS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Numerous synthetic aperture radar (SAR) satellites around planet Earth are monitoring the fragile ecosystem and serving for the sustainable development of human society. They are working with various modes and configurations (e.g., frequencies, polarimetry, interferometry, ultrawide-swath SAR, constellations, etc.). Global-scale products derived from SAR data are available for geoscientists (e.g., global DEM, global urban footprint and global forest map), providing new insight into the earth systems. A new radar remote sensing paradigm is emerging in the big data era. How to process SAR data at large scales in time-series efficiently, how to retrieve information intelligently in real-time, and how to assess and boost Sustainable Development Goals (SDGs) with SAR observations, among other issues, are challenges we are facing now and in the coming years.

This Special Issue aims to publish studies related to techniques for processing SAR data at large scales and time-series, as well as various applications for the implementation and evaluation SDGs. We would like to invite you to contribute by submitting articles about your recent research, experimental work, as well as reviews related to these issues. Contributions may include, but are not limited to, the following topics:

Present and future SAR programs and satellites.
High-performance computing for SAR data.
AI for SAR data.
Time-series and ultra-wide-swath SAR data processing.
PolSAR for environment and resource monitoring.
InSAR for geo-hazards and the Earth’s surface monitoring.
SAR for Sustainable Development Goals.

Prof. Dr. Chao Wang
Prof. Dr. Alberto Moreira
Prof. Dr. Mihai Datcu
Dr. Fan Wu
Dr. Ruya Xiao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

SAR
big data
artificial intelligence
high-performance computing
Sustainable Development Goals (SDGs)
Earth’s surface

Related Special Issue

SAR in Big Data Era III in Remote Sensing (2 articles)

Published Papers (14 papers)

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Research

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23 pages, 56742 KiB

Open AccessArticle

Parallel Optimization for Large Scale Interferometric Synthetic Aperture Radar Data Processing

by Weikang Zhang, Haihang You, Chao Wang, Hong Zhang and Yixian Tang

Remote Sens. 2023, 15(7), 1850; https://doi.org/10.3390/rs15071850 - 30 Mar 2023

Cited by 2 | Viewed by 1578

Abstract

Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the handing of SAR big data poses significant challenges for related algorithms and pipeline, particularly in large-scale SAR data processing. In addition, InSAR algorithms are highly complex, and their task dependencies are intricate. There is a lack of efficient optimization models and task scheduling for InSAR pipeline. In this paper, we design parallel time-series InSAR processing models based on multi-thread technology for high efficiency in processing InSAR big data. These models concentrate on parallelizing critical algorithms that have high complexity, with a focus on deconstructing two computationally intensive algorithms through loop unrolling. Our parallel models have shown a significant improvement of 10–20 times in performance. We have also developed a parallel optimization tool, Simultaneous Task Automatic Runtime (STAR), which utilizes a data flow optimization strategy with thread pool technology to address the problem of low CPU utilization resulting from multiple modules and task dependencies in the InSAR processing pipeline. STAR provides a data-driven pipeline and enables concurrent execution of multiple tasks, with greater flexibility to keep the CPU busy and further improve CPU utilization through predetermined task flow. Additionally, a supercomputing-based system has been constructed for processing massive InSAR scientific big data and providing technical support for nationwide surface deformation measurement, in accordance with the framework of time series InSAR data processing. Using this system, we processed InSAR data with the volumes of 500 TB and 700 TB in 5 and 7 days, respectively. Finally we generated two maps of land surface deformation all over China. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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25 pages, 13509 KiB

Open AccessArticle

Feedback-Assisted Automatic Target and Clutter Discrimination Using a Bayesian Convolutional Neural Network for Improved Explainability in SAR Applications

by Nicholas Blomerus, Jacques Cilliers, Willie Nel, Erik Blasch and Pieter de Villiers

Remote Sens. 2022, 14(23), 6096; https://doi.org/10.3390/rs14236096 - 01 Dec 2022

Cited by 4 | Viewed by 1559

Abstract

In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by “black box” algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users’ ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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16 pages, 5235 KiB

Open AccessArticle

A 3D Space-Time Non-Local Mean Filter (NLMF) for Land Changes Retrieval with Synthetic Aperture Radar Images

by Antonio Pepe

Remote Sens. 2022, 14(23), 5933; https://doi.org/10.3390/rs14235933 - 23 Nov 2022

Cited by 3 | Viewed by 1768

Abstract

Sequences of multi-temporal synthetic aperture radar (SAR) images are routinely used for land-use land-change (LULC) applications, allowing the retrieval of accurate and up-to-date information on the state of the Earth’s surface and its temporal variations. Change detection (CD) methods that rely on the exploitation of SAR data are, generally, made of three distinctive steps: (1) pre-processing of the SAR images; (2) comparison of the pairs of SAR images; and (3) the automatic extraction of the “changed areas”, employing proper thresholding algorithms. Within this general framework, the reduction in speckle noise effects, which can be obtained by applying spatial multi-looking operations and ad hoc noise filters, is fundamental for the better detecting and classifying of changed regions. Usually, speckle noise filters are singularly and independently applied to every SAR image without the consideration of their inherent temporal relationships. In particular, most use local (spatial) approaches based on determining and averaging SAR backscattered signals related to neighboring SAR pixels. In this work, conversely, we explore the potential of a joint 3D space-time non-local mean filter (NLMF), which relies on the discrimination of similar features in a block of non-local SAR pixels extracted from the same or different SAR images. The theory behind non-local-mean filters is, first, shortly revised. Then, the developed space-time NLMF is applied to a real test case for the purposes of identifying flooded zones due to the massive inundations that hit the Kerala region, India, during the summer of 2018. To this aim, a set of 18 descending SAR images collected from the European (EU) Copernicus Sentinel-1 (S-1) sensor was exploited. The performance of the developed NLMF has also been assessed. It is worth remarking that the proposed method can be applied for the purposes of analyzing a heterogenous set of natural and/or artificial disastrous conditions. Further, it can also be helpful during the pre-processing stages of the sequences of SAR images for the purposes of CD applications. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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22 pages, 25512 KiB

Open AccessArticle

An Innovative Supervised Classification Algorithm for PolSAR Image Based on Mixture Model and MRF

by Mingliang Liu, Yunkai Deng, Chuanzhao Han, Wentao Hou, Yao Gao, Chunle Wang and Xiuqing Liu

Remote Sens. 2022, 14(21), 5506; https://doi.org/10.3390/rs14215506 - 01 Nov 2022

Cited by 1 | Viewed by 1212

Abstract

The Wishart mixture model is an effective tool for characterizing the statistical distribution of polarimetric synthetic aperture radar (PolSAR) data. However, due to the difficulty in determining the equivalent number of looks, the Wishart mixture model has some problems in terms of practicality. In addition, the flexibility of the Wishart mixture model needs to be improved for complicated scenes. To improve the practicality and flexibility, a new mixture model named the relaxed Wishart mixture model (RWMM) is proposed. In RWMM, the equivalent number of looks is no longer considered a constant for the whole PolSAR image but a variable that varies between different clusters. Next, an innovative algorithm named RWMM-Markov random field (RWMM-MRFt) for supervised classification is proposed. A new selection criterion for adaptive neighborhood systems is proposed in the algorithm to improve the classification performance. The new criterion makes effective use of PolSAR scattering information to select the most suitable neighborhood for each center pixel in PolSAR images. Three datasets, including one simulated image and two real PolSAR images, are utilized in the experiment. The maximum likelihood classification results demonstrate the flexibility of the proposed RWMM for modeling PolSAR data. The proposed selection criterion shows superior performance than the span-based selection criterion. Among the mixture model-based MRF classification algorithms, the proposed RWMM-MRFt algorithm has the highest classification accuracy, and the corresponding classification maps have better anti-noise performance. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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20 pages, 9141 KiB

Open AccessArticle

Water Surface Mapping from Sentinel-1 Imagery Based on Attention-UNet3+: A Case Study of Poyang Lake Region

by Chaowei Jiang, Hong Zhang, Chao Wang, Ji Ge and Fan Wu

Remote Sens. 2022, 14(19), 4708; https://doi.org/10.3390/rs14194708 - 21 Sep 2022

Cited by 8 | Viewed by 2064

Abstract

The mapping of water surfaces is important for water resource and flood monitoring. Synthetic Aperture Radar (SAR) images can be used to monitor water bodies and detect floods over large areas. To address the problem of low identification accuracy in different time phases and different scales of water area, a water surface mapping method based on Attention-UNet3+ with SAR images is proposed in this paper. In the model, full-scale skip connections are introduced for combining feature maps from different scales and improving the accuracy of narrow water identification; the spatial attention module is used to obtain the importance of each connected feature, which can reduce the number of false alarms caused by speckle noise and water shadows in SAR data; the deep supervision module is used to learn hierarchical representative features from comprehensive aggregated feature maps to provide the periodic output capability of the model and meet the needs of rapid and large-scale water identification. The effectiveness of Attention-UNet3+ is verified by experiments in the Poyang Lake region with Sentinel-1 SAR images. The results show that the proposed Attention-UNet3+ outperforms the conventional threshold segmentation and deep learning models such as UNet, Deepvlabv3+, and SegNet, with an average IOU/Kappa value of 0.9502/0.9698. Multitemporal Sentinel-1 images in 2021 covering Poyang Lake are used for time series water surface mapping with the proposed method, and it is found that the detected water area of Poyang Lake has a good correlation with the corresponding water level values at observation stations. The Pearson coefficients are about 0.96. The above results indicate that the proposed method achieves good water surface mapping performance. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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20 pages, 10873 KiB

Open AccessArticle

Built-Up Area Extraction from GF-3 SAR Data Based on a Dual-Attention Transformer Model

by Tianyang Li, Chao Wang, Fan Wu, Hong Zhang, Sirui Tian, Qiaoyan Fu and Lu Xu

Remote Sens. 2022, 14(17), 4182; https://doi.org/10.3390/rs14174182 - 25 Aug 2022

Cited by 6 | Viewed by 1463

Abstract

Built-up area (BA) extraction using synthetic aperture radar (SAR) data has emerged as a potential method in urban research. Currently, typical deep-learning-based BA extractors show high false-alarm rates in the layover areas and subsurface bedrock, which ignore the surrounding information and cannot be directly applied to large-scale BA mapping. To solve the above problems, a novel transformer-based BA extraction framework for SAR images is proposed. Inspired by SegFormer, we designed a BA extractor with multi-level dual-attention transformer encoders. First, the hybrid dilated convolution (HDC) patch-embedding module keeps the surrounding information of the input patches. Second, the channel self-attention module is designed for dual-attention transformer encoders and global modeling. The multi-level structure is employed to produce the coarse-to-fine semantic feature map of BAs. About 1100 scenes of Gaofen-3 (GF-3) data and 200 scenes of Sentinel-1 data were used in the experiment. Compared to UNet, PSPNet, and SegFormer, our model achieved an 85.35% mean intersection over union (mIoU) and 94.75% mean average precision (mAP) on the test set. The proposed framework achieved the best results in both mountainous and plain terrains. The experiments using Sentinel-1 shows that the proposed method has a good generalization ability with different SAR data sources. Finally, the BA map of China for 2020 was obtained with an overall accuracy of about 86%, which shows high consistency with the global urban footprint. The above experiments proved the effectiveness and robustness of the proposed framework in large-scale BA mapping. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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20 pages, 127816 KiB

Open AccessArticle

Object-Oriented Unsupervised Classification of PolSAR Images Based on Image Block

by Binbin Han, Ping Han and Zheng Cheng

Remote Sens. 2022, 14(16), 3953; https://doi.org/10.3390/rs14163953 - 14 Aug 2022

Cited by 1 | Viewed by 1195

Abstract

Land Use and Land Cover (LULC) classification is one of the tasks of Polarimetric Synthetic Aperture Radar (PolSAR) images’ interpretation, and the classification performance of existing algorithms is highly sensitive to the class number, which is inconsistent with the reality that LULC classification should have multiple levels of detail in the same image. Therefore, an object-oriented unsupervised classification algorithm for PolSAR images based on the image block is proposed. Firstly, the image is divided into multiple non-overlapping image blocks, and

h / q / g r a y

-Wishart classification is performed in each block. Secondly, each cluster obtained is regarded as an object, and the affinity matrix of objects is calculated in the global image. Finally, the objects are merged into the specified class number by density peak clustering (DPC), and the adjacent objects at the block boundary are checked and forced to merge. Experiments are carried out with the measured data of the airborne AIRSAR and E-SAR and the spaceborne GF-3. The experimental results show that the proposed algorithm achieves good classification results under a variety of class numbers. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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25 pages, 8364 KiB

Open AccessArticle

Oil Spill Detection with Dual-Polarimetric Sentinel-1 SAR Using Superpixel-Level Image Stretching and Deep Convolutional Neural Network

by Jin Zhang, Hao Feng, Qingli Luo, Yu Li, Yu Zhang, Jian Li and Zhoumo Zeng

Remote Sens. 2022, 14(16), 3900; https://doi.org/10.3390/rs14163900 - 11 Aug 2022

Cited by 6 | Viewed by 2484

Abstract

Synthetic aperture radar (SAR) has been widely applied in oil spill detection on the sea surface due to the advantages of wide area coverage, all-weather operation, and multi-polarization characteristics. Sentinel-1 satellites can provide dual-polarized SAR data, and they have high potential for successful application to oil spill detection. However, the characteristics of the sea surface and oil film on different images are not the same when imaging at different locations and in different conditions, which leads to the inconsistent accuracy of these images with the application of the current oil spill detection methods. In order to avoid the above limitation, we propose an oil spill detection method using image stretching based on superpixels and a convolutional neural network. Experiments were carried out on eight Sentinel-1 dual-pol data, and the optimal superpixel number and image stretching parameters are discussed. Mean intersection over union (MIoU) was used to evaluate classification accuracy. The proposed method could effectively improve the classification accuracy; when the expansion and inhibition coefficients of image stretching were set to 1.6 and 1.2 respectively, the experiments achieved a maximum MIoU of 85.4%, 7.3% higher than that without image stretching. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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20 pages, 19311 KiB

Open AccessArticle

Research on the Applicability of DInSAR, Stacking-InSAR and SBAS-InSAR for Mining Region Subsidence Detection in the Datong Coalfield

by Yaozong Xu, Tao Li, Xinming Tang, Xiang Zhang, Hongdong Fan and Yuewen Wang

Remote Sens. 2022, 14(14), 3314; https://doi.org/10.3390/rs14143314 - 09 Jul 2022

Cited by 22 | Viewed by 3255

Abstract

Intensive and large-scale underground coal mining has caused geological disasters such as local ground subsidence, cracks and collapse in the Datong coalfield, China, inducing serious threats to local residents. Interferometric synthetic aperture radar (InSAR) has the capability of surface deformation detection with high precision in vast mountainous areas. DInSAR, stacking-InSAR and SBAS-InSAR are commonly used InSAR-related deformation analysis methods. They can provide effective support for mine ecological security monitoring and prevent disasters. We use the three methods to conduct the deformation observation experiments in the Datong coalfield. Sentinel-1A data from November 2020 to October 2021 are used. As a result, a total of 256 deformations in the Datong coalfield were successfully detected by the three methods, of which 218 are mining deformations, accounting for 85% of the total deformations. By comparing the results of the three methods, we found that DInSAR, stacking-InSAR, and SBAS-InSAR detected 130, 256, and 226 deformations in the Datong coalfield, respectively, while the deformations caused by coal mining were 128, 218, and 190. DInSAR results with long spatiotemporal baselines are seriously incoherent. SBAS-InSAR results of displacement rate are more precise than stacking-InSAR, and the mean standard deviation is 1.0 mm/a. However, for areas with lush vegetation or low coherence, SBAS-InSAR has poor performance. The detection deformation area results of DInSAR and SBAS-InSAR are subsets of stacking-InSAR. The displacement rates obtained by stacking-InSAR and SBAS-InSAR are consistent; the mean difference in the displacement rate between the two methods is 2.7 mm/a, and the standard deviation is 5.1 mm/a. The mining deformation locations and their shapes in the study area can be identified with high efficiency and power by stacking-InSAR. Therefore, with a comprehensive understanding of the advantages and limitations of the three methods, stacking-InSAR can be an effective and fast method to identify the level, location and range of mining deformation in lush mountainous areas. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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19 pages, 4826 KiB

Open AccessArticle

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

by Fanyi Zhang, Xin Tian, Haibo Zhang and Mi Jiang

Remote Sens. 2022, 14(13), 3022; https://doi.org/10.3390/rs14133022 - 23 Jun 2022

Cited by 19 | Viewed by 3177

Abstract

Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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23 pages, 105652 KiB

Open AccessArticle

Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm

by Bingqian Chen, Hao Yu, Xiang Zhang, Zhenhong Li, Jianrong Kang, Yang Yu, Jiale Yang and Lu Qin

Remote Sens. 2022, 14(3), 788; https://doi.org/10.3390/rs14030788 - 08 Feb 2022

Cited by 16 | Viewed by 2968

Abstract

After a coal mine is closed, the coal rock mass could undergo weathering deterioration and strength reduction due to factors such as stress and groundwater, which in turn changes the stress and bearing capacity of the fractured rock mass in the abandoned goaf, leading to secondary or multiple surface deformations in the goaf. Currently, the spatiotemporal evolution pattern of the surface deformation of closed mines remains unclear, and there is no integrated monitoring and prediction model for closed mines. Therefore, this study proposed to construct an integrated monitoring and prediction model for closed mines using small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) and a deep learning-based long short-term memory (LSTM) neural network algorithm to achieve evolution pattern and dynamic prediction of spatiotemporal surface deformation of closed mines. Taking a closed mine in the western part of Xuzhou, China, as an example, based on Sentinel-1A SAR data between 21 December 2015, and 11 January 2021, SBAS InSAR technology was used to obtain the spatiotemporal evolution pattern of the surface during the 5 years after mine closure. The results showed that the ground surface subsided in the early stage of mine closure and then uplifted. In 5 years, the maximum subsidence rate in the study area is −43 mm/a, and the cumulative maximum subsidence is 310 mm; the maximum uplift rate is 29 mm/a, and the cumulative maximum uplift is 135 mm. Moreover, the maximum tilt and curvature are 3.5 mm/m and 0.19 mm/m², respectively, which are beyond the safety thresholds of buildings; thus, continuous monitoring is necessary. Based on the evolution pattern of surface deformation, the surface deformation prediction model was proposed by integrating SBAS InSAR and an LSTM neural network. The experiment results showed that the LSTM neural network can accurately predict the deformation trend, with a maximum root mean square error (RMSE) of 5.1 mm. Finally, the relationship between the residual surface deformation and time after mine closure was analyzed, and the mechanisms of surface subsidence and uplift were discussed, which provide a theoretical reference for better understanding the surface deformation process of closed mines and the prevention of surface deformation. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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12 pages, 7323 KiB

Open AccessTechnical Note

Identifying Potential Landslides on Giant Niexia Slope (China) Based on Integrated Multi-Remote Sensing Technologies

by Xiujun Dong, Tao Yin, Keren Dai, Saied Pirasteh, Guanchen Zhuo, Zhiyu Li, Bing Yu and Qiang Xu

Remote Sens. 2022, 14(24), 6328; https://doi.org/10.3390/rs14246328 - 14 Dec 2022

Cited by 5 | Viewed by 1478

Abstract

The Niexia slope, located in Danba County, Sichuan Province, China, with steep slope terrain and dense vegetation coverage, has a height difference of about 3002 m. A traditional manual survey cannot be performed here, and single remote sensing technology is not comprehensive enough to identify potential landslides on such high and steep slopes. In this paper, an integrated approach with multi-remote sensing techniques was proposed to identify potential landslides of the Niexia slope, which combined Interferometry Synthetic Aperture Radar (InSAR), airborne Light Detection and Ranging (LiDAR), and optical remote sensing technologies. InSAR technology was used to monitor the small displacements of the whole slope, and three potential landslides on Niexia slope were identified. The maximum cumulative displacement reached up to 11.9 cm over 1 year. Subsequently, high-resolution optical remote sensing images acquired by remote sensing satellites and a Digital Elevation Model (DEM) without vegetation influence obtained by LiDAR were used to finely interpret the sign of landslide micro-geomorphology and to determine the potential landslide geometry boundaries. As a result, four and nine potential landslides with landslide micro-geomorphic features were identified, respectively. Finally, the identification results of the three techniques were fused and analyzed to assess the potential landslides on the Niexia slope. We compared the results from multi-remote sensing technologies, showing that the three techniques have advantages and disadvantages in terms of monitoring objects, monitoring range, and monitoring accuracy. The integrated use of these three technologies can identify and monitor potential landslides more comprehensively, which could play an important role in the future. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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15 pages, 15976 KiB

Open AccessTechnical Note

Removing InSAR Topography-Dependent Atmospheric Effect Based on Deep Learning

by Chen Chen, Keren Dai, Xiaochuan Tang, Jianhua Cheng, Saied Pirasteh, Mingtang Wu, Xianlin Shi, Hao Zhou and Zhenhong Li

Remote Sens. 2022, 14(17), 4171; https://doi.org/10.3390/rs14174171 - 25 Aug 2022

Cited by 5 | Viewed by 2232

Abstract

Atmospheric effects are among the primary error sources affecting the accuracy of interferometric synthetic aperture radar (InSAR). The topography-dependent atmospheric effect is particularly noteworthy in reservoir areas for landslide monitoring utilizing InSAR, which must be effectively corrected to complete the InSAR high-accuracy measurement. This paper proposed a topography-dependent atmospheric correction method based on the Multi-Layer Perceptron (MLP) neural network model combined with topography and spatial data information. We used this proposed approach for the atmospheric correction of the interferometric pairs of Sentinel-1 images in the Baihetan dam. We contrasted the outcomes with those obtained using the generic atmospheric correction online service for InSAR (GACOS) correction and the traditional linear model correction. The results indicated that the MLP neural network model correction reduced the phase standard deviation of the Sentinel-1 interferogram by an average of 64% and nearly eliminated the phase-elevation correlation. Both comparisons outperformed the GACOS correction and the linear model correction. Through two real-world examples, we demonstrated how slopes with displacements, which were previously obscured by a significant topography-dependent atmospheric delay, could be successfully and clearly identified in the interferograms following the correction by the MLP neural network. The topography-dependent atmosphere can be better corrected using the MLP neural network model suggested in this paper. Unlike the previous model, this proposed approach could be adjusted to fit each interferogram, regardless of how much of the topography-dependent atmosphere was present. In order to improve the effectiveness of DInSAR and time-series InSAR solutions, it can be applied immediately to the interferogram to retrieve the effective displacement information that cannot be identified before the correction. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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17 pages, 3320 KiB

Open AccessTechnical Note

Monitoring Potential Geological Hazards with Different InSAR Algorithms: The Case of Western Sichuan

by Zezhong Zheng, Chuhang Xie, Yong He, Mingcang Zhu, Weifeng Huang and Tianming Shao

Remote Sens. 2022, 14(9), 2049; https://doi.org/10.3390/rs14092049 - 25 Apr 2022

Cited by 14 | Viewed by 2434

Abstract

In recent years, the number of geological disasters in Sichuan Province has significantly increased due to the influence of earthquakes and extreme climate, as well as the disturbance to the geological environment by human activities. Thus, geological disaster monitoring is particularly important, which can provide some scientific basis for disaster prevention and reduction. In this paper, the interferometric synthetic aperture radar (InSAR) technology was introduced to monitor potential geological hazards, taking parts of Dujiangyan City, Wenchuan County, and Mao County in Sichuan Province, China as examples. Firstly, the data such as Sentinel-1A Terrain Observation with Progressive Scans (TOPS) Synthetic Aperture Radar (SAR) images and Precision Orbit Determination (POD) precise orbit ephemerides from 2018 to 2020, high-resolution optical satellite images and Digital Elevation Model (DEM) were collected. Secondly, the Differential InSAR (D-InSAR), Persistent Scatterer InSAR (PS-InSAR), Small Baseline Subset InSAR (SBAS-InSAR), Offset-Tracking, and Distributed Scatterer InSAR (DS-InSAR) algorithms were used to invert the surface deformation of the study area. Thirdly, according to the deformation results obtained by experiments, we used GF-1 and GF-2 optical images as a reference and combine the experimental results of InSAR algorithms to delineate the areas affected by geological disasters. A total of 49 geological disaster areas were obtained, mainly including landslides, collapses, and debris flow. Through field verification, the overall accuracy rate of InSAR deformation monitoring achieved 69.23%, and the accuracy rate of new potential hazards monitoring reached 63.64%. Among all InSAR methods, the DS-InSAR method outperformed and monitored the geological disaster areas well. Finally, the study area was divided into three elevation intervals and the applicability of different InSAR algorithms in different elevation intervals was discussed. Full article

(This article belongs to the Special Issue SAR in Big Data Era II)

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