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Deep Learning Meets SAR [article]

Xiao Xiang Zhu, Sina Montazeri, Mohsin Ali, Yuansheng Hua, Yuanyuan Wang, Lichao Mou, Yilei Shi, Feng Xu, Richard Bamler
<span title="2021-01-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2020-06-17">2020</span> <span class="release-stage" >pre-print</span> <span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.10027v1">arXiv:2006.10027v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sjdqhdimfjdufazmwihs7zftku">fatcat:sjdqhdimfjdufazmwihs7zftku</a> </span>
With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.  ...  of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions.  ...  For the purpose of matching SAR and optical images, Merkle et al.  ... 
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Citation

Xiao Xiang Zhu, Sina Montazeri, Mohsin Ali, Yuansheng Hua, Yuanyuan Wang, Lichao Mou, Yilei Shi, Feng Xu, Richard Bamler. "Deep Learning Meets SAR." arXiv (2021)

Multisensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery

Patrick Ebel, Andrea Meraner, Michael Schmitt, Xiao Xiang Zhu
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4odsbtjobjalfki6xxabjpdu6y" style="color: black;">IEEE Transactions on Geoscience and Remote Sensing</a> </i> &nbsp;

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Our data set is the first publically available to contain a global sample of coregistered radar and optical observations, cloudy and cloud-free.  ...  We target the challenge of generalization by curating a large novel data set for training new cloud removal approaches and evaluate two recently proposed performance metrics of image quality and diversity  ...  ACKNOWLEDGMENT The authors would like to thank ESA and the Copernicus Program for making the Sentinel observations accessed for this submission publicly available.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tgrs.2020.3024744">doi:10.1109/tgrs.2020.3024744</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tj5iggofxre7baviik6a3wuiju">fatcat:tj5iggofxre7baviik6a3wuiju</a> </span>
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Patrick Ebel, Andrea Meraner, Michael Schmitt, Xiao Xiang Zhu. "Multisensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery." IEEE Transactions on Geoscience and Remote Sensing 59.7 (2020) 1-13

Road Segmentation in SAR Satellite Images With Deep Fully Convolutional Neural Networks

Corentin Henry, Seyed Majid Azimi, Nina Merkle
<span title="">2018</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q2cxanfrsvclzbz73u2mnqrxru" style="color: black;">IEEE Geoscience and Remote Sensing Letters</a> </i> &nbsp;

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<span title="2018-08-16">2018</span> <span class="release-stage">accepted</span>
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Remote sensing is extensively used in cartography. As transportation networks grow and change, extracting roads automatically from satellite images is crucial to keep maps up-to-date.  ...  Few works study the potential of deep learning techniques, despite their successful applications to optical imagery.  ...  The extraction of roads in SAR satellite images has been researched for several decades [1] and is generally addressed in the following manner: road candidates are extracted from SAR images using a feature  ... 
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Citation

Corentin Henry, Seyed Majid Azimi, Nina Merkle. "Road Segmentation in SAR Satellite Images With Deep Fully Convolutional Neural Networks." IEEE Geoscience and Remote Sensing Letters 15.12 (2018) 1-5

A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration

Shuo Li, Xiaolei Lv, Jian Ren, Jian Li
<span title="2022-01-28">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;

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The co-registration between SAR and optical images is a challenging task because of the speckle noise of SAR and the nonlinear radiation distortions (NRD), particularly in the one-look situation.  ...  NMS-SSD fast template matching and fast sample consensus (FSC) algorithm are used to further complete and optimize matching.  ...  Then, they used the Siamese convolutional network to learn pixelwise deep dense features to further improve the robustness of the network [34] .  ... 
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Citation

Shuo Li, Xiaolei Lv, Jian Ren, Jian Li. "A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration." Remote Sensing 14.3 (2022) 630

Efficient Instance Segmentation Paradigm for Interpreting SAR and Optical Images

Fan Fan, Xiangfeng Zeng, Shunjun Wei, Hao Zhang, Dianhua Tang, Jun Shi, Xiaoling Zhang
<span title="2022-01-23">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;

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EISP mainly consists of the Swin Transformer to construct the hierarchical features of SAR and optical images, the context information flow (CIF) for interweaving the semantic features from the bounding  ...  In this paper, an efficient instance segmentation paradigm (EISP) for interpreting the synthetic aperture radar (SAR) and optical images is proposed.  ...  Acknowledgments: The authors would like to thank the anonymous reviewers and editors for their selfless help to improve our manuscript.  ... 
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Citation

Fan Fan, Xiangfeng Zeng, Shunjun Wei, Hao Zhang, Dianhua Tang, Jun Shi, Xiaoling Zhang. "Efficient Instance Segmentation Paradigm for Interpreting SAR and Optical Images." Remote Sensing 14.3 (2022) 531

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer
<span title="">2017</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xwpybjcqyffjdipbdviyet75c4" style="color: black;">IEEE Geoscience and Remote Sensing Magazine</a> </i> &nbsp;

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<span title="2017-10-11">2017</span> <span class="release-stage" >pre-print</span>
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In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously  ...  More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges,  ...  SARptical is a unique data set for SAR and optical image matching in dense urban areas.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mgrs.2017.2762307">doi:10.1109/mgrs.2017.2762307</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ec7b32lpdnhvzbdz2uoayw6anq">fatcat:ec7b32lpdnhvzbdz2uoayw6anq</a> </span>
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Citation

Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer. "Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources." IEEE Geoscience and Remote Sensing Magazine 5.4 (2017) 8-36

Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles

Martina Pastorino, Alessandro Montaldo, Luca Fronda, Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia
<span title="2021-02-25">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;

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decision tree ensembles for pixelwise modeling.  ...  This problem is crucial in the study of satellite imagery and jointly involves multiresolution and multisensor image fusion.  ...  Acknowledgments: The authors wish to thank CNES for partial financial funding, DigitalGlobe Inc. for the QuickBird data, Google Crisis Response for the GeoEye-1 data, and the Italian Space Agency (ASI)  ... 
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Citation

Martina Pastorino, Alessandro Montaldo, Luca Fronda, Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. "Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles." Remote Sensing (2021) 849

Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery [article]

Patrick Ebel, Andrea Meraner, Michael Schmitt, Xiaoxiang Zhu
<span title="2020-09-16">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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We target the challenge of generalization by curating a large novel data set for training new cloud removal approaches and evaluate on two recently proposed performance metrics of image quality and diversity  ...  Our data set is the first publically available to contain a global sample of co-registered radar and optical observations, cloudy as well as cloud-free.  ...  We would finally like to thank Lloyd Hughes for having shared his artifact detection pre-processing code with us. After  ... 
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Patrick Ebel, Andrea Meraner, Michael Schmitt, Xiaoxiang Zhu. "Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery." arXiv (2020)

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b2n2tpw5ang73osulebz6bm4ju" style="color: black;">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</a> </i> &nbsp;

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., +, JSTARS 2020 6260-6276 Change Detection in Heterogeneous Optical and SAR Remote Sensing Images Via Deep Homogeneous Feature Fusion.  ...  ., +, JSTARS 2020 6260-6276 Change Detection in Heterogeneous Optical and SAR Remote Sensing Images Via Deep Homogeneous Feature Fusion.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
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"2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2020) 6537-6633

Table of Contents

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b2n2tpw5ang73osulebz6bm4ju" style="color: black;">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</a> </i> &nbsp;

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Cheng 3006 Automatic Stitching for Hyperspectral Images Using Robust Feature Matching and Elastic Warp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Hostache 896 Identification of Agricultural Row Features Using Optical Data for Scattering and Reflectance Modeling Over Periodic Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Xia 3735 Change Detection in Heterogeneous Optical and SAR Remote Sensing Images Via Deep Homogeneous Feature Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
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"Table of Contents." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2020) i-xxii

Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series

Ricardo Dal Molin, Paola Rizzoli
<span title="2022-03-12">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;

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The monitoring of land cover and land use patterns is pivotal in the joint effort to fight deforestation in the Amazon and study its relation to climate change effects with respect to anthropogenic activities  ...  As input features, we relied on both multi-temporal backscatter and interferometric coherences at different temporal baselines.  ...  Acknowledgments: The authors would like to thank our colleagues Andrea Pulella and Francescopaolo Sica for the collaboration at the early stages of this study and Luca Dell'Amore and José-Luis Bueso-Bello  ... 
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Ricardo Dal Molin, Paola Rizzoli. "Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series." Remote Sensing 14.6 (2022) 1381

A Convolutional Neural Network Architecture for Sentinel-1 and AMSR2 Data Fusion

David Malmgren-Hansen, Leif Toudal Pedersen, Allan Aasbjerg Nielsen, Matilde Brandt Kreiner, Roberto Saldo, Henning Skriver, John Lavelle, Jorgen Buus-Hinkler, Klaus Harnvig Krane
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4odsbtjobjalfki6xxabjpdu6y" style="color: black;">IEEE Transactions on Geoscience and Remote Sensing</a> </i> &nbsp;

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In the first approach, concentrations are thresholded to be encoded in a standard binary fashion, and in the second approach, concentrations are used as the target probability directly.  ...  It can be concluded that CNNs are suitable for multisensor fusion even with sensors that differ in resolutions by large factors, such as in the case of Sentinel-1 SAR and AMSR2.  ...  One example is the land cover classification where fusion of SAR and optical images can lead to improved results [10] .  ... 
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David Malmgren-Hansen, Leif Toudal Pedersen, Allan Aasbjerg Nielsen, Matilde Brandt Kreiner, Roberto Saldo, Henning Skriver, John Lavelle, Jorgen Buus-Hinkler, Klaus Harnvig Krane. "A Convolutional Neural Network Architecture for Sentinel-1 and AMSR2 Data Fusion." IEEE Transactions on Geoscience and Remote Sensing (2020) 1-13

Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks [article]

Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro, Enrico Magli
<span title="2020-07-04">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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The proposed method is trained employing only noisy SAR images and can therefore learn features of real SAR images rather than synthetic data.  ...  In the literature, this issue is tackled by resorting to either synthetically speckled optical images, which exhibit different properties with respect to true SAR images, or multi-temporal SAR images,  ...  Our method bypasses the problem of training a CNN on synthetically-speckled optical images, thus avoiding any domain gap and enabling learning of features from real SAR images.  ... 
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Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro, Enrico Magli. "Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks." arXiv (2020)

HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline [article]

Konrad Heidler, Lichao Mou, Celia Baumhoer, Andreas Dietz, Xiao Xiang Zhu
<span title="2021-03-02">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast,  ...  Training is made efficient by employing deep supervision on side predictions at multiple resolutions.  ...  Using SAR data for the task of coastline extraction also imposes some challenges. The speckle present in SAR images makes it harder to pinpoint the exact boundary between land and sea.  ... 
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Konrad Heidler, Lichao Mou, Celia Baumhoer, Andreas Dietz, Xiao Xiang Zhu. "HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline." arXiv (2021)

SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal

Patrick Ebel, Yajin Xu, Michael Schmitt, Xiao Xiang Zhu
<span title="">2022</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4odsbtjobjalfki6xxabjpdu6y" style="color: black;">IEEE Transactions on Geoscience and Remote Sensing</a> </i> &nbsp;

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optical and radar images.  ...  This work addresses the challenge of optical satellite image reconstruction and cloud removal by proposing a novel multimodal and multitemporal data set called SEN12MS-CR-TS.  ...  The authors would also like to thank Rewanth Ravindran for assisting us in the data curation process.  ... 
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Patrick Ebel, Yajin Xu, Michael Schmitt, Xiao Xiang Zhu. "SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal." IEEE Transactions on Geoscience and Remote Sensing (2022) 1-14

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