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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Unsupervised Multitemporal Spectral Unmixing for Detecting Multiple Changes in Hyperspectral Images
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Unsupervised Multitemporal Spectral Unmixing for Detecting Multiple Changes in Hyperspectral Images

机译：用于检测高光谱图像中多个变化的无监督多时间光谱分解

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摘要

This paper presents a novel multitemporal spectral unmixing (MSU) approach to address the challenging multiple-change detection problem in bitemporal hyperspectral (HS) images. Differently from the state-of-the-art methods that are mainly designed at a pixel level, the proposed technique investigates the spectral–temporal variations at a subpixel level. The considered change detection (CD) problem is analyzed in a multitemporal domain, where a bitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct (MT-EMs) are extracted according to an automatic and unsupervised technique. Then, a change analysis strategy is designed to distinguish the change and no-change MT-EMs. An endmember-grouping scheme is applied to the changed MT-EMs to detect the unique change classes. Finally, the considered multiple-change detection problem is solved by analyzing the abundances of the change and no-change classes and their contribution to each pixel. The proposed approach has been validated on both simulated and real multitemporal HS data sets presenting multiple changes. Experimental results confirmed the effectiveness of the proposed method.
机译：本文提出了一种新颖的多时相光谱解混（MSU）方法，以解决双向高光谱（HS）图像中具有挑战性的多变化检测问题。与主要在像素级别设计的最新方法不同，所提出的技术研究了子像素级别的频谱-时间变化。在多时域中分析所考虑的变化检测（CD）问题，其中定义了双时相光谱混合模型以分析像素内的光谱组成。区别（MT-EM）是根据自动和无监督的技术提取的。然后，设计变更分析策略以区分变更和无变更MT-EM。将最终成员分组方案应用于更改后的MT-EM，以检测唯一的更改类。最后，通过分析变化量和不变量类别的丰度及其对每个像素的贡献，解决了所考虑的多次变化检测问题。所提出的方法已经在模拟和真实的多时间HS数据集上进行了验证，这些数据集呈现出多种变化。实验结果证实了该方法的有效性。

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