高光谱成像
像素
计算机科学
冗余(工程)
自编码
模式识别(心理学)
人工智能
约束(计算机辅助设计)
空间相关性
空间分析
遥感
深度学习
数学
地理
几何学
操作系统
电信
作者
Lijuan Su,Jun Li,Yan Yuan,Qi‐Yue Chen
出处
期刊:Remote Sensing
[MDPI AG]
日期:2023-06-02
卷期号:15 (11): 2898-2898
被引量:4
摘要
Hyperspectral unmixing, which decomposes mixed pixels into the endmembers and corresponding abundances, is an important image process for the further application of hyperspectral images (HSIs). Lately, the unmixing problem has been solved using deep learning techniques, particularly autoencoders (AEs). However, the majority of them are based on the simple linear mixing model (LMM), which disregards the spectral variability of endmembers in different pixels. In this article, we present a multi-attention AE network (MAAENet) based on the extended LMM to address the issue of the spectral variability problem in real scenes. Moreover, the majority of AE networks ignore the global spatial information in HSIs and operate pixel- or patch-wise. We employ attention mechanisms to design a spatial–spectral attention (SSA) module that can deal with the band redundancy in HSIs and extract global spatial features through spectral correlation. Moreover, noticing that the mixed pixels are always present in the intersection of different materials, a novel sparse constraint based on spatial homogeneity is designed to constrain the abundance and abstract local spatial features. Ablation experiments are conducted to verify the effectiveness of the proposed AE structure, SSA module, and sparse constraint. The proposed method is compared with several state-of-the-art unmixing methods and exhibits competitiveness on both synthetic and real datasets.
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