Edge-Guided Hyperspectral Change Detection.

Hyperspectral change detection (HCD) is widely applied in various domains, such as accurate agriculture, disaster assessment, land use, and environmental monitoring. Most HCD methods aim at extracting and classifying the spectral variation features with dimension reduction and machine-learning methods. Different from previous work, this letter proposes an edge-guided HCD method. Specifically, a subtraction operation is adopted to extract difference hyperspectral image (HSI). Then, the edge-preserving filtering is performed on the difference HSI to extract spectral–spatial features. Next, the number of the extracted features is diminished through the kernel principal component analysis (PCA). Finally, the fused features are input into a spectral classifier followed by the edge-preserving filtering to obtain the final change detection result. Experiments on several HCD datasets demonstrate that the proposed method can consistently outperform other advanced approaches in both subjective and objective evaluations when only a limited number of labeled samples are available. [ABSTRACT FROM AUTHOR]