Hyperspectral Change Detection Based on Multiple Morphological Profiles
Zengfu Hou, Wei Li, Lu Li, Ran Tao, Qian Du
Abstract
With the increasing availability of multitemporal hyperspectral imagery, hyperspectral change detection under heterogeneous backgrounds is a challenging task. Due to the complexity of background features, traditional change detection algorithms in the spectral domain cannot effectively detect changed features. A novel method using multiple morphological profiles (MMPs) is proposed for hyperspectral change detection to make full use of spatial information. In the designed framework, first, the max-tree/min-tree strategy is applied to extract different attributes of multitemporal hyperspectral images (HSIs), i.e., area attribute and height attribute. Second, a spectral angle weighted-based local absolute distance (SALA) method is designed to reconstruct the discriminative spectral domain. Then, the absolute distance (AD) is adopted to extract changes in constructed feature domain. Finally, a change map is obtained by guided filtering. Experiments conducted on four real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.