Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval

Park, Seonho; Rysz, Maciej; Dipple, Kathleen M.; Pardalos, Pãnos M.

doi:10.1007/978-3-031-21225-3_3

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Both extracted features will be processed conjointly by a GCN and a fully connected network for an optimized classification. In a more standard fashion, the paper [31] trains the network MoCo [18] with a homography transformation for data augmentation [32]. It is chosen as an easily applicable augmentation suitable for SAR instead of most optical data augmentation methods.…”

Section: Ssl In Sarmentioning

confidence: 99%

Self-Supervised Learning Based Anomaly Detection in Synthetic Aperture Radar Imaging

Muzeau

Ren

Angelliaume

et al. 2022

IEEE Open J. Signal Process.

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In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviate from their surroundings without prior knowledge of their characteristics. This method deals with the crucial problems related to the presence of speckle, the spatial correlation structures in SAR images, and the lack of annotated data to train a detection algorithm. Our proposed method aims to address these issues through a self-supervised learning algorithm. First, we propose to mitigate the SAR speckle through the deep learning SAR2SAR algorithm. We then develop an Adversarial Autoencoder (AAE) to reconstruct anomaly-free SAR images from despeckled data taking into account potential spatial correlation structures. Finally, a change detection processing step is applied between the input and the output to detect anomalies. Experiments are performed to show the advantages of our method compared to the conventional Reed-Xiaoli algorithm, highlighting the importance of an efficient despeckling pre-processing step.

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Section: Ssl In Sarmentioning

confidence: 99%

Self-Supervised Learning Based Anomaly Detection in Synthetic Aperture Radar Imaging

Muzeau

Ren

Angelliaume

et al. 2022

IEEE Open J. Signal Process.

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Geographic mapping with unsupervised multi-modal representation learning from VHR images and POIs

Bai

Huang

Zhang

et al. 2023

ISPRS Journal of Photogrammetry and Remote Sensing

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Self-Supervised Learning in Remote Sensing: A review

Wang

Albrecht

Braham

et al. 2022

IEEE Geosci. Remote Sens. Mag.

102

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In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While there has been a big success in computer vision, most of the potential of SSL in the domain of earth observation remains locked. In this paper, we provide an introduction to, and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for earth observation (SSL4EO) to pave the way for fruitful interaction of both domains.

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Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval

Cited by 3 publications

References 27 publications

Self-Supervised Learning Based Anomaly Detection in Synthetic Aperture Radar Imaging

Self-Supervised Learning Based Anomaly Detection in Synthetic Aperture Radar Imaging

Geographic mapping with unsupervised multi-modal representation learning from VHR images and POIs

Self-Supervised Learning in Remote Sensing: A review

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