Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images

• URL: http://arxiv.org/abs/2205.02049v1
• Date: Wed, 4 May 2022 13:16:48 GMT
• Title: Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images
• Authors: Pallavi Jain, Bianca Schoen-Phelan, Robert Ross
• Abstract summary: Self-Supervised learning (SSL) has become the new state-of-art in several domain classification and segmentation tasks. This work proposes RSDnet, which applies the distillation network (BYOL) in the remote sensing (RS) domain.
• Score: 5.994412766684843
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Self-Supervised learning (SSL) has become the new state-of-art in several domain classification and segmentation tasks. Of these, one popular category in SSL is distillation networks such as BYOL. This work proposes RSDnet, which applies the distillation network (BYOL) in the remote sensing (RS) domain where data is non-trivially different from natural RGB images. Since Multi-spectral (MS) and synthetic aperture radar (SAR) sensors provide varied spectral and spatial resolution information, we utilised them as an implicit augmentation to learn invariant feature embeddings. In order to learn RS based invariant features with SSL, we trained RSDnet in two ways, i.e., single channel feature learning and three channel feature learning. This work explores the usefulness of single channel feature learning from random MS and SAR bands compared to the common notion of using three or more bands. In our linear evaluation, these single channel features reached a 0.92 F1 score on the EuroSAT classification task and 59.6 mIoU on the DFC segmentation task for certain single bands. We also compared our results with ImageNet weights and showed that the RS based SSL model outperforms the supervised ImageNet based model. We further explored the usefulness of multi-modal data compared to single modality data, and it is shown that utilising MS and SAR data learn better invariant representations than utilising only MS data.

Related papers

• Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images [18.371087310792287]
  This work aims to utilize the strong visual recognition capabilities of Vision Foundation Models (VFMs) to improve the change detection of high-resolution Remote Sensing Images (RSIs) We employ the visual encoder of FastSAM, an efficient variant of the SAM, to extract visual representations in RS scenes. To utilize the semantic representations that are inherent to SAM features, we introduce a task-agnostic semantic learning branch to model the semantic latent in bi-temporal RSIs. The resulting method, SAMCD, obtains superior accuracy compared to the SOTA methods and exhibits a sample-efficient learning ability that is comparable to semi-
  arXiv  Detail & Related papers  (2023-09-04T08:23:31Z)
• A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery [4.397725469518669]
  Self-supervised learning (SSL) enables the models to learn a representation from orders of magnitude more unlabelled data. This work has designed a novel SSL framework that is capable of learning representation from both spectra-spatial information of unlabelled data.
  arXiv  Detail & Related papers  (2023-06-27T23:50:43Z)
• Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation [119.009033745244]
  This paper presents a Self-supervised Low-Rank Network ( SLRNet) for single-stage weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS) SLRNet uses cross-view self-supervision, that is, it simultaneously predicts several attentive LR representations from different views of an image to learn precise pseudo-labels. Experiments on the Pascal VOC 2012, COCO, and L2ID datasets demonstrate that our SLRNet outperforms both state-of-the-art WSSS and SSSS methods with a variety of different settings.
  arXiv  Detail & Related papers  (2022-03-19T09:19:55Z)
• Multi-level Second-order Few-shot Learning [111.0648869396828]
  We propose a Multi-level Second-order (MlSo) few-shot learning network for supervised or unsupervised few-shot image classification and few-shot action recognition. We leverage so-called power-normalized second-order base learner streams combined with features that express multiple levels of visual abstraction. We demonstrate respectable results on standard datasets such as Omniglot, mini-ImageNet, tiered-ImageNet, Open MIC, fine-grained datasets such as CUB Birds, Stanford Dogs and Cars, and action recognition datasets such as HMDB51, UCF101, and mini-MIT.
  arXiv  Detail & Related papers  (2022-01-15T19:49:00Z)
• Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images [108.79667788962425]
  salient object detection in optical remote sensing images (RSI-SOD) remains to be a challenging emerging topic. We propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD. In MCCM, we consider multiple types of features that are critical to RSI-SOD, including foreground features, edge features, background features, and global image-level features.
  arXiv  Detail & Related papers  (2021-12-02T04:46:40Z)
• Remote Sensing Images Semantic Segmentation with General Remote Sensing Vision Model via a Self-Supervised Contrastive Learning Method [13.479068312825781]
  We propose Global style and Local matching Contrastive Learning Network (GLCNet) for remote sensing semantic segmentation. Specifically, the global style contrastive module is used to learn an image-level representation better. The local features matching contrastive module is designed to learn representations of local regions which is beneficial for semantic segmentation.
  arXiv  Detail & Related papers  (2021-06-20T03:03:40Z)
• Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model [36.993630058695345]
  We propose a shared and specific feature learning (S2FL) model to decomposing multimodal RS data into modality-shared and modality-specific components. To better assess multimodal baselines and the newly-proposed S2FL model, three multimodal RS benchmark datasets, i.e., Houston2013 -- hyperspectral and multispectral data, Berlin -- hyperspectral and synthetic aperture radar (SAR) data, Augsburg -- hyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification.
  arXiv  Detail & Related papers  (2021-05-21T08:14:21Z)
• Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets [99.92564298432387]
  This work studies Hyperspectral image (HSI) super-resolution (SR) HSI SR is characterized by high-dimensional data and a limited amount of training examples. This exacerbates the undesirable behaviors of neural networks such as memorization and sensitivity to out-of-distribution samples.
  arXiv  Detail & Related papers  (2021-01-19T12:19:53Z)
• Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples [11.025191332244919]
  We introduce new self-supervised learning (SSL) mechanism to obtain the high-performance pre-training model for RSIs scene classification from large unlabeled data. Experiments on three commonly used RSIs scene classification datasets demonstrated that this new learning paradigm outperforms the traditional dominant ImageNet pre-trained model. The insights distilled from our studies can help to foster the development of SSL in the remote sensing community.
  arXiv  Detail & Related papers  (2020-10-02T09:27:19Z)
• X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data [69.37597254841052]
  We propose a novel cross-modal deep-learning framework called X-ModalNet. X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-24T15:29:41Z)
• ResNeSt: Split-Attention Networks [86.25490825631763]
  We present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification.
  arXiv  Detail & Related papers  (2020-04-19T20:40:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.