Multitemporal Intrinsic Image Decomposition With Temporal–Spatial Energy Constraints for Remote Sensing Image Analysis

Due to interference with remote imaging by some natural factors, the multitemporal analysis ability is limited by the spectral drift between images. In this article, a new approach to optimize the existing multitemporal analysis system is proposed: multitemporal intrinsic image decomposition (MIID). The MIID method is designed to extract common spectral reflectance from multitemporal images. With MIID, multitemporal classification, changing detection, and index extracting will become extremely easy and more accurate. Firstly, without considering land cover change, the general MIID framework is proposed by adding local temporal–spatial energy constraints in traditional intrinsic images decomposition. On this basis, an improved MIID method with change detection (CD) (CD-MIID) capability is proposed to make the model adapt to the land cover change situation. Finally, specific steps of how to use MIID methods in the multitemporal analysis are given. Multitemporal multispectral/hyperspectral remote sensing images from GF-1, GF-2, GF-5, Landsat TM, and two groups of captured datasets with reflectance truth map are used to evaluate the performance. The experimental results show the following two points: first, the MIID methods achieve better extraction results of spectral reflectance. Second, the proposed MIID methods have better performance both on multitemporal classification and CD.