SAR ATR based on displacement- and rotation-insensitive CNN

Du, Kangning; Deng, Yunkai; Wang, Robert; Zhao, Tianjie; Li, Ning

doi:10.1080/2150704x.2016.1196837

Cited by 69 publications

(47 citation statements)

References 3 publications

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“…This section reviews some of the relevant studies in this area. Many authors applied CNN to SAR ATR and tested on MSTAR dataset, e.g., [43][44][45][46], etc.…”

Section: B Interpretation Of Sar Imagesmentioning

confidence: 99%

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

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,346

1,204

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a "black-box" solution? There are controversial opinions in the remote sensing community. 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 simple to start with. 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, such as climate change and urbanization. Following this wave of success and thanks to the increased availability of data and computational resources, the use of deep learning in remote sensing is finally taking off in remote sensing as well. Remote sensing data bring some new challenges for deep learning, since satellite image analysis raises some unique questions that translate into challenging new scientific questions:• Remote sensing data are often multi-modal, e.g. from optical (multi-and hyperspectral) and synthetic aperture radar (SAR) sensors, where both the imaging geometries and the content are completely different. Data and information fusion uses these complementary

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“…This section reviews some of the relevant studies in this area. Many authors applied CNN to SAR ATR and tested on MSTAR dataset, e.g., [43][44][45][46], etc.…”

Section: B Interpretation Of Sar Imagesmentioning

confidence: 99%

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

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,346

1,204

View full text Add to dashboard Cite

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“…Cheng et al 226 incorporated a rotation-invarint layer into a DL CNN architecture to detect objects in satellite imagery. Du et al 127 developed a displacement-and rotation-insensitive deep CNN for SAR Automated Target Recognition (ATR) processing that is trained by augmented dataset and specialized training procedure.…”

Section: Non-traditional Heterogeneous Data Sourcesmentioning

confidence: 99%

“…3D (depth and shape) analysis [107][108][109][110][111][112][113][114][115] Advanced driver assistance systems [116][117][118][119][120] Animal detection 121 Anomaly detection 122 Automated Target Recognition [123][124][125][126][127][128][129][130][131][132][133][134] Change detection [135][136][137][138][139] Classification Data fusion 191 Dimensionality reduction 192,193 Disaster analysis/assessment 194 Environment and water analysis [195][196][197][198] Geo-information extraction 199 Human detection [200][201][202][203] Image denoising/enhancement 204,…”

Section: References Area Referencesmentioning

confidence: 99%

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Ball

Anderson

Chan

2017

J. Appl. Remote Sens

541

331

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Abstract. In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Hughes et al [19] identified corresponding patches in SAR and optical images with pseudosiamese CNN, to identify corresponding patches in high-resolution optical and SAR remote sensing imagery. ough the above methods could achieve relatively good performance, two key problems still remain unsolved in SAR ATR [17,20]. e first challenging problem is effective model design, which is mainly impacted by objective function and cost function design, super-high dimensional parameters optimization, and so on.…”

Section: Introductionmentioning

confidence: 99%

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L²-Regularization

Zhai

Deng

et al. 2019

Computational Intelligence and Neuroscience

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Though Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) via Convolutional Neural Networks (CNNs) has made huge progress toward deep learning, some key issues still remain unsolved due to the lack of sufficient samples and robust model. In this paper, we proposed an efficient transferred Max-Slice CNN (MS-CNN) with L2-Regularization for SAR ATR, which could enrich the features and recognize the targets with superior performance. Firstly, the data amplification method is presented to reduce the computational time and enrich the raw features of SAR targets. Secondly, the proposed MS-CNN framework with L2-Regularization is trained to extract robust features, in which the L2-Regularization is incorporated to avoid the overfitting phenomenon and further optimizing our proposed model. Thirdly, transfer learning is introduced to enhance the feature representation and discrimination, which could boost the performance and robustness of the proposed model on small samples. Finally, various activation functions and dropout strategies are evaluated for further improving recognition performance. Extensive experiments demonstrated that our proposed method could not only outperform other state-of-the-art methods on the public and extended MSTAR dataset but also obtain good performance on the random small datasets.

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SAR ATR based on displacement- and rotation-insensitive CNN

Cited by 69 publications

References 3 publications

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

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

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L²-Regularization

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SAR ATR based on displacement- and rotation-insensitive CNN

Cited by 69 publications

References 3 publications

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

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

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L2-Regularization

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Product

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Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L²-Regularization