Total-variation-regularized local spectral unmixing for hyperspectral image super-resolution

Shao-lei ZHANG; Guang-yuan FU; Hong-qiao WANG; Yu-qing ZHAO

doi:10.3788/OPE.20192712.2683

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Total-variation-regularized local spectral unmixing for hyperspectral image super-resolution

Information Sciences | 更新时间：2020-07-09

- Total-variation-regularized local spectral unmixing for hyperspectral image super-resolution
- Optics and Precision Engineering Vol. 27, Issue 12, Pages: 2683-2692(2019)
- 作者机构：
  
  火箭军工程大学信息工程系, 陕西西安 710025
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192712.2683
  CLC： TP391.4
- Received：26 March 2019，
  
  Accepted：12 May 2019，
  
  Published：25 December 2019
- 稿件说明：
移动端阅览
Shao-lei ZHANG, Guang-yuan FU, Hong-qiao WANG, et al. Total-variation-regularized local spectral unmixing for hyperspectral image super-resolution[J]. Optics and precision engineering, 2019, 27(12): 2683-2692.
DOI：

Shao-lei ZHANG, Guang-yuan FU, Hong-qiao WANG, et al. Total-variation-regularized local spectral unmixing for hyperspectral image super-resolution[J]. Optics and precision engineering, 2019, 27(12): 2683-2692. DOI： 10.3788/OPE.20192712.2683.

摘要

融合相同场景下低分辨率高光谱图像和高分辨率多光谱图像生成高分辨率高光谱图像是获取空间域和光谱域的综合场景信息一种重要方法。为充分利用图像的光谱信息和空间信息，提出了向量总变差正则的局部光谱解混的高光谱图像超分辨方法。本文基于耦合狄利克雷自编码分别从高光谱图像和多光谱图像提取光谱特征和对应的空间信息。耦合网络的解码部分能有效地保留光谱特征，集成局部低秩约束和向量总变差约束的正则项可以充分利用多光谱图像空间结构信息从而提取稳定的丰度矩阵，最小化角相似性可以有效减少光谱失真，最后通过端元和丰度的线性组合生成高分辨率的高光谱图像。实验表明，在CAVE和Harvard数据集上重构误差分别达到3.78和1.66，光谱角映射分别为6.57和3.03，较其他方法有明显提高。本文方法能充分利用图像的空间性质，具有更好的高光谱图像超分辨效果。

Abstract

Fusing a low-resolution Hyperspectral Image (HSI)with its corresponding high-resolution Multispectral Image (MSI) to obtain a high-resolution HSI is amajortechnique for capturing comprehensive scene information in both spatial and spectral domains. To exploit fully the spectral and spatial information of an image

an algorithm based on total-variation-regularized local spectral unmixing for HSI super-resolution was proposed in this study. Spectral features and corresponding spatial information were extracted from both HSIs and MSIs through coupled encode-decode networks

respectively. The decoder of the coupled network could effectively preserve spectral features

and regular terms integrating local low-rank and vector total variation constraints could make full use of spatial structure information in MSIs to extract a stable abundance matrix.Finally

the angular differences between representations were minimized to reduce the spectral distortion.Experimental results reveal that the reconstruction errors in CAVE and Harvard datasets reach 3.78 and 1.66

respectively

and the spectral angle maps are 6.57 and 3.03

respectively

thus outperforming the state-of-the-art methods. The proposed algorithm can make full use of the spatial properties and thus produces a better HIS super-resolution effect.

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references

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