Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues

• URL: http://arxiv.org/abs/2312.11119v1
• Date: Mon, 18 Dec 2023 11:37:19 GMT
• Title: Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues
• Authors: Xingxing Yang, Jie Chen, Zaifeng Yang
• Abstract summary: Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands. We propose to investigate the inter-dependencies in their respective hyperspectral space. These embedded features can be fully exploited by querying the inter-channel correlations.
• Score: 6.580484964018551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands. As such, we propose to investigate the chromatic inter-dependencies in their respective hyperspectral embedding space. These embedded features can be fully exploited by querying the inter-channel correlations in a combinatorial manner, with the unique and complementary information efficiently fused into the final prediction. We found such independent modeling and combinatorial excavation mechanisms are extremely beneficial to uncover marginal spectral features, especially in the long wavelength bands. In addition, we have proposed a spatio-spectral attention block and a spectrum-fusion attention module, which greatly facilitates the excavation and fusion of information at both semantically long-range levels and fine-grained pixel levels across all dimensions. Extensive quantitative and qualitative experiments show that our method (dubbed CESST) achieves SOTA performance. Code for this project is at: https://github.com/AlexYangxx/CESST.

Related papers

• Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learning [15.86617273658407]
  We propose an unsupervised blind fusion method for hyperspectral and multispectral images based on Tucker decomposition and spatial spectral manifold learning (DTDNML) We show that this method enhances the accuracy and efficiency of hyperspectral and multispectral fusion on different remote sensing datasets.
  arXiv  Detail & Related papers  (2024-09-15T08:58:26Z)
• HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space Model [5.505983410956103]
  Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level. This paper proposes an efficient self-supervised HTD method with a pyramid state space model (SSM), named HTD-Mamba. Experiments conducted on four public datasets demonstrate that the proposed method outperforms state-of-the-art methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2024-07-09T13:21:26Z)
• HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model [88.13261547704444]
  Hyper SIGMA is a vision transformer-based foundation model for HSI interpretation. It integrates spatial and spectral features using a specially designed spectral enhancement module. It shows significant advantages in scalability, robustness, cross-modal transferring capability, and real-world applicability.
  arXiv  Detail & Related papers  (2024-06-17T13:22:58Z)
• SSF-Net: Spatial-Spectral Fusion Network with Spectral Angle Awareness for Hyperspectral Object Tracking [21.664141982246598]
  Hyperspectral video (HSV) offers valuable spatial, spectral, and temporal information simultaneously. Existing methods primarily focus on band regrouping and rely on RGB trackers for feature extraction. In this paper, a spatial-spectral fusion network with spectral angle awareness (SST-Net) is proposed for hyperspectral (HS) object tracking.
  arXiv  Detail & Related papers  (2024-03-09T09:37:13Z)
• Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-Resolution [47.12985199570964]
  We propose a novel cross-scope spatial-spectral Transformer (CST) to investigate long-range spatial and spectral similarities for single hyperspectral image super-resolution. Specifically, we devise cross-attention mechanisms in spatial and spectral dimensions to comprehensively model the long-range spatial-spectral characteristics. Experiments over three hyperspectral datasets demonstrate that the proposed CST is superior to other state-of-the-art methods both quantitatively and visually.
  arXiv  Detail & Related papers  (2023-11-29T03:38:56Z)
• Multi-Spectral Image Stitching via Spatial Graph Reasoning [52.27796682972484]
  We propose a spatial graph reasoning based multi-spectral image stitching method. We embed multi-scale complementary features from the same view position into a set of nodes. By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features.
  arXiv  Detail & Related papers  (2023-07-31T15:04:52Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• 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)
• Mixed Attention Network for Hyperspectral Image Denoising [9.723155514555765]
  We present a Mixed Attention Network (MAN) that simultaneously considers the inter- and intra-spectral correlations as well as the interactions between low- and high-level spatial-spectral meaningful features. Our MAN outperforms existing state-of-the-art methods on simulated and real noise settings while maintaining a low cost of parameters and running time.
  arXiv  Detail & Related papers  (2023-01-27T04:02:35Z)
• 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.