Multi-temporal change detection based on China’s domestic hyperspectral remote sensing satellite images

Comparing with the multispectral remote sensing image, hyperspectral image (HSI) has higher spectral resolution, a near continuous spectral signature, thus can represent fine spectral variations that occurred in the temporal domain. This allows more spectral changes to be detected, especially major changes that reflected on the overall spectral signature (associating with the abrupt land-cover transitions), as well as subtle changes that reflect only on a portion of the spectral signature (associating with the change of physicochemical properties of the land-cover classes). Currently, there are some available hyperspectral change detection (CD) data sets. However, they have the following drawbacks. First, there is a lack of diversity in the data source; all data sets were created using the Hyperion sensor mounted on the E0-1 satellite. Second, these data sets mainly concentrate on the river and agriculture scenes, which lose their diversity for representing different land-covers. In this paper, we construct three new change detection data sets by using the multitemporal images acquired by the China's new generation of hyperspectral satellites, i.e., OHS, GF-5 and ZY1-02D. These data sets present various event-driven land-cover changes, such as new building construction, crop replacements, and the expansion of energy facilities. Then a novel unsupervised hyperspectral change detection approach is proposed based on the intrinsic image decomposition (IID). Experimental results confirmed the effectiveness of the proposed approach in terms of higher overall accuracy by comparing with the reference techniques.