A Superpixel-by-Superpixel Clustering Framework for Hyperspectral Change Detection

Abstract

Hyperspectral image change detection (HSI-CD) is an interesting task in the Earth’s remote sensing community. However, current HSI-CD methods are feeble at detecting subtle changes from bitemporal HSIs, because the decision boundary is partially stretched by strong changes so that subtle changes are ignored. In this paper, we propose a superpixel-by-superpixel clustering framework (SSCF), which avoids the confusion of different changes and thus reduces the impact on decision boundaries. Wherein the simple linear iterative clustering (SLIC) is employed to spatially segment the different images (DI) of the bitemporal HSIs into superpixels. Meanwhile, the Gaussian mixture model (GMM) extracts uncertain pixels from the DI as a rough threshold for clustering. The final CD results are obtained by passing the determined superpixels and uncertain pixels through K-means. The experimental results of two spaceborne bitemporal HSIs datasets demonstrate competitive efficiency and accuracy in the proposed SSCF.