Informative change detection by unmixing for hyperspectral images
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The document discusses the potential of using spectral unmixing on multi-temporal hyperspectral images for effective change detection, including insights into sub-pixel variations of materials over time. It highlights the limited previous studies due to the lack of multi-temporal hyperspectral data and the challenges of coherent application. The letter evaluates the advantages of this approach, backed by experimental results from both synthetic and real datasets.
Informative change detection by unmixing for hyperspectral images
- 1. INFORMATIVE CHANGE DETECTION BY UNMIXING FOR HYPERSPECTRAL IMAGES ABSTRACT Applying spectral unmixing on a series of multi temporal hyperspectral images for change detection has the potential to reveal important sub pixel-level information, such as the abundance variation of each underlying material in a given location or the change in the distribution of materials throughout the scene, with time or resulting from significant events such as a natural disaster. However, change detection by spectral unmixing for hyperspectral images has not been extensively studied up to now, and most studies have been limited to specific cases and data sets. This is caused by the scarcity of real multi temporal hyperspectral data and the inherent difficulties in applying unmixing to multi temporal hyperspectral data in a coherent way. In this letter, we investigate change detection for hyperspectral images by spectral unmixing and systematically present the advantages that can be gained by using such an approach, supported by experimental studies conducted on carefully prepared synthetic data sets and also with real data sets.