Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images

• URL: http://arxiv.org/abs/2110.09744v2
• Date: Thu, 21 Oct 2021 14:40:14 GMT
• Title: Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images
• Authors: Ge Zhang, Shaohui Mei, Mingyang Ma, Yan Feng, and Qian Du
• Abstract summary: A spectral variability augmented sparse unmixing model (SVASU) is proposed, in which the spectral variability is extracted explicitly. It is noted that the spectral variability library and the intrinsic spectral library are all constructed from the In-situ observed image. Experimental results over both synthetic and real-world data sets demonstrate that the augmented decomposition by spectral variability significantly improves the unmixing performance.
• Score: 20.703976519242094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral images as the product of endmember and abundance, which has been widely used in hyperspectral imagery analysis. However, the influence of light, acquisition conditions and the inherent properties of materials, results in that the identified endmembers can vary spectrally within a given image (construed as spectral variability). To address this issue, recent methods usually use a priori obtained spectral library to represent multiple characteristic spectra of the same object, but few of them extracted the spectral variability explicitly. In this paper, a spectral variability augmented sparse unmixing model (SVASU) is proposed, in which the spectral variability is extracted for the first time. The variable spectra are divided into two parts of intrinsic spectrum and spectral variability for spectral reconstruction, and modeled synchronously in the SU model adding the regular terms restricting the sparsity of abundance and the generalization of the variability coefficient. It is noted that the spectral variability library and the intrinsic spectral library are all constructed from the In-situ observed image. Experimental results over both synthetic and real-world data sets demonstrate that the augmented decomposition by spectral variability significantly improves the unmixing performance than the decomposition only by spectral library, as well as compared to state-of-the-art algorithms.

Related papers

• Weighted Sum of Segmented Correlation: An Efficient Method for Spectra Matching in Hyperspectral Images [3.454872059813283]
  This study introduces the weighted Sum of Segmented Correlation method, which calculates correlation indices between various segments of a library and a test spectrum. The effectiveness of this approach is evaluated for mineral identification in hyperspectral images from both Earth and Martian surfaces.
  arXiv  Detail & Related papers  (2024-06-18T18:51:00Z)
• Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer [14.594398447576188]
  In the hyperspectral image classification (HSIC) task, the most commonly used model validation paradigm is partitioning the training-test dataset through pixel-wise random sampling. In our experiments, we found that the high accuracy was reached because the training and test datasets share a lot of information. We propose a spectral-spatial axial aggregation transformer model, namely SaaFormer, that preserves generalization across dataset partitions.
  arXiv  Detail & Related papers  (2023-06-29T07:55:43Z)
• DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model [46.68717345017946]
  Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing. We propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD) Our method can achieve comparable results to those fully supervised methods requiring numerous samples.
  arXiv  Detail & Related papers  (2023-05-21T09:21:41Z)
• Spectral Feature Augmentation for Graph Contrastive Learning and Beyond [64.78221638149276]
  We present a novel spectral feature argumentation for contrastive learning on graphs (and images) For each data view, we estimate a low-rank approximation per feature map and subtract that approximation from the map to obtain its complement. This is achieved by the proposed herein incomplete power iteration, a non-standard power regime which enjoys two valuable byproducts (under mere one or two iterations) Experiments on graph/image datasets show that our spectral feature augmentation outperforms baselines.
  arXiv  Detail & Related papers  (2022-12-02T08:48:11Z)
• Spectral Unmixing of Hyperspectral Images Based on Block Sparse Structure [1.491109220586182]
  This paper presents an innovative spectral unmixing approach for hyperspectral images (HSIs) based on block-sparse structure and sparse Bayesian learning strategy.
  arXiv  Detail & Related papers  (2022-04-10T09:37:41Z)
• A spectral-spatial fusion anomaly detection method for hyperspectral imagery [7.155465756606866]
  spectral fusion anomaly detection (SSFAD) method is proposed for hyperspectral imagery. New detector is designed to extract the local similarity spatial features of patch images in spatial domain.
  arXiv  Detail & Related papers  (2022-02-24T03:54:48Z)
• Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition [78.37855832568569]
  We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function. We show numerical and experimental results for spectral CT materials decomposition.
  arXiv  Detail & Related papers  (2021-03-25T15:20:10Z)
• Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography [20.03088101097943]
  Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs) Due to the limited photons and counting rate in the corresponding spectral fraction, the reconstructed spectral images usually suffer from severe noise. In this paper, a fourth-order nonlocal tensor decomposition model for spectral CT image reconstruction (FONT-SIR) is proposed.
  arXiv  Detail & Related papers  (2020-10-27T15:14:36Z)
• Cross-Spectral Periocular Recognition with Conditional Adversarial Networks [59.17685450892182]
  We propose Conditional Generative Adversarial Networks, trained to con-vert periocular images between visible and near-infrared spectra. We obtain a cross-spectral periocular performance of EER=1%, and GAR>99% @ FAR=1%, which is comparable to the state-of-the-art with the PolyU database.
  arXiv  Detail & Related papers  (2020-08-26T15:02:04Z)
• Two-Dimensional Single- and Multiple-Quantum Correlation Spectroscopy in Zero-Field Nuclear Magnetic Resonance [55.41644538483948]
  We present single- and multiple-quantum correlation $J$-spectroscopy detected in zero magnetic field using a Rb vapor-cell magnetometer. At zero field the spectrum of ethanol appears as a mixture of carbon isotopomers, and correlation spectroscopy is useful in separating the two composite spectra.
  arXiv  Detail & Related papers  (2020-04-09T10:02:45Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)

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.