REMOTE SENSING CHANGE DETECTION BY
INCLUSION OF MULTITEMPORAL TEXTURE
LI Shu-kun 1, LI Pei-jun 1, CHENG Tao 2
1. Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China;
2. Department of Earth and Atmosphere, University of Alberta Edmonton, Alberta T6G2R3, Canada
Change detection based on remote sensing is one of the important aspects in remote sensing application. Traditional remote sensing change detection methods usually use spectral information alone and ignore the correlation between multitemporal images. This paper proposes to quantitatively express the temporal correlation between multitemporal images by multivariate texture, which is measured by pseudo cross variogram, a geostatistical tool. The obtained multitemporal texture, as an additional band, is incorporated into multitemporal classification for change detection. The results show that, compared with the result of using spectral information alone, the inclusion of multitemporal classification in change detection can significantly improve the overall accuracy. The experimental results also validate the effectiveness of the proposed method.