A NOVEL UNSUPERVISED CHANGE DETECTION APPROACH BASED ON SPECTRAL TRANSFORMATION FOR MULTISPECTRAL IMAGES

Change detection (CD) for multispectral remote sensing images is an important approach to observe the changes of the earth. However, the same object usually has different spectra in multi-temporal images, which is one of the biggest challenges for CD. To overcome this problem, a novel unsupervised CD approach based on spectral transformation and joint spectral-spatial feature learning (STCD) is proposed for multispectral images in this paper. By exploring the relationship between imaging environment and the object spectra, the spectral transformation is used to suppress the phenomenon of "same object with different spectra". Besides, a detection network with joint spectral-spatial feature learning is designed to extract the spectral-spatial features simultaneously to make the CD algorithm more robust. Both theoretical analyses and experiment results proved that the proposed STCD method is superior to the state-of-the-art unsupervised methods on multispectral images CD.