HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection

• URL: http://arxiv.org/abs/2207.09634v1
• Date: Wed, 20 Jul 2022 03:26:03 GMT
• Title: HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection
• Authors: Meiqi Hu, Chen Wu, and Liangpei Zhang
• Abstract summary: HyperNet is a pixel-level self-supervised hyperspectral spatial-spectral understanding network. It accomplishes pixel-wise feature representation for effective hyperspectral change detection. Six hyperspectral datasets have been adopted to test the validity and generalization of proposed HyperNet.
• Score: 19.774857440703038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The fast development of self-supervised learning lowers the bar learning feature representation from massive unlabeled data and has triggered a series of research on change detection of remote sensing images. Challenges in adapting self-supervised learning from natural images classification to remote sensing images change detection arise from difference between the two tasks. The learned patch-level feature representations are not satisfying for the pixel-level precise change detection. In this paper, we proposed a novel pixel-level self-supervised hyperspectral spatial-spectral understanding network (HyperNet) to accomplish pixel-wise feature representation for effective hyperspectral change detection. Concretely, not patches but the whole images are fed into the network and the multi-temporal spatial-spectral features are compared pixel by pixel. Instead of processing the two-dimensional imaging space and spectral response dimension in hybrid style, a powerful spatial-spectral attention module is put forward to explore the spatial correlation and discriminative spectral features of multi-temporal hyperspectral images (HSIs), separately. Only the positive samples at the same location of bi-temporal HSIs are created and forced to be aligned, aiming at learning the spectral difference-invariant features. Moreover, a new similarity loss function named focal cosine is proposed to solve the problem of imbalanced easy and hard positive samples comparison, where the weights of those hard samples are enlarged and highlighted to promote the network training. Six hyperspectral datasets have been adopted to test the validity and generalization of proposed HyperNet. The extensive experiments demonstrate the superiority of HyperNet over the state-of-the-art algorithms on downstream hyperspectral change detection tasks.

Related papers

• Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation [55.9217962930169]
  We present S2ADet, an object detector that harnesses the rich spectral and spatial complementary information inherent in hyperspectral images. S2ADet surpasses existing state-of-the-art methods, achieving robust and reliable results.
  arXiv  Detail & Related papers  (2023-06-14T09:01:50Z)
• Exploring Invariant Representation for Visible-Infrared Person Re-Identification [77.06940947765406]
  Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy. In this paper, we address the problem from both image-level and feature-level in an end-to-end hybrid learning framework named robust feature mining network (RFM) Experiment results on two standard cross-spectral person re-identification datasets, RegDB and SYSU-MM01, have demonstrated state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-02-02T05:24:50Z)
• Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection [32.23764287942984]
  We have proposed a Multi-Temporal spatial-spectral Comparison Network for hyperspectral anomalous change detection (MTC-NET) The whole model is a deep siamese network, aiming at learning the prevalent spectral difference resulting from the complex imaging conditions from the hyperspectral images by contrastive learning. The experiments on the "Viareggio 2013" datasets demonstrate the effectiveness of proposed MTC-NET.
  arXiv  Detail & Related papers  (2022-05-23T15:41:27Z)
• A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images [12.222830717774118]
  A dual neighborhood hypergraph neural network is proposed in this article. The proposed method comprises better effectiveness and robustness compared to many state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-02-27T02:39:08Z)
• Learning Hierarchical Graph Representation for Image Manipulation Detection [50.04902159383709]
  The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural Networks (CNNs) to capture the tampering artifacts left in the images. We propose a hierarchical Graph Convolutional Network (HGCN-Net), which consists of two parallel branches.
  arXiv  Detail & Related papers  (2022-01-15T01:54:25Z)
• Unsupervised Change Detection in Hyperspectral Images using Feature Fusion Deep Convolutional Autoencoders [15.978029004247617]
  The proposed work aims to build a novel feature extraction system using a feature fusion deep convolutional autoencoder. It is found that the proposed method clearly outperformed the state of the art methods in unsupervised change detection for all the datasets.
  arXiv  Detail & Related papers  (2021-09-10T16:52:31Z)
• Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction [16.530040002441694]
  We propose an unsupervised spatial-spectral network to reconstruct hyperspectral images only from the compressive snapshot measurement. Our network can achieve better reconstruction results than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-12-18T12:29:04Z)
• D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization [108.8592577019391]
  Image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. We propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection.
  arXiv  Detail & Related papers  (2020-12-03T10:54:02Z)
• Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning [23.203687716051697]
  A robust unsupervised approach is proposed for small area change detection from synthetic aperture radar (SAR) images. A multi-scale superpixel reconstruction method is developed to generate a difference image (DI) A two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes.
  arXiv  Detail & Related papers  (2020-11-22T12:50:08Z)
• Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning [62.52242684874278]
  Cross-modality distribution of spatial and spectral information makes the problem challenging. We propose a novel textitlightweight deep neural network-based framework, namely PZRes-Net. Our framework learns a high resolution and textitzero-centric residual image, which contains high-frequency spatial details of the scene.
  arXiv  Detail & Related papers  (2020-06-18T06:32:11Z)
• Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery [79.69449412334188]
  In this paper, we investigate how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches. We introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images.
  arXiv  Detail & Related papers  (2020-05-18T14:25:50Z)

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.