关键词: 变化检测, 面向对象, 变化矢量, 主成分分析

Abstract:

Land cover/use change in Taizhou City is studied in this paper. Due to human or natural factors, land cover/use constantly changes. With the development of remote sensing technology, change detection is one of the important applications on resource and environment remote sensing. In this paper, we propose a Change Vector-PCA analysis method based on CVA to extract change information in the study area. First, we compute the relative difference between t1 and t2 remote sensing image of Taizhou to obtain the difference image, then employ PCA to this difference image and select principal components for the extraction of change information. Next, the image objects are obtained by multi-scale segmentation combining with the spectral and spatial characteristics and suitable segmentation scale. And, the change information is extracted automatically with the rules derived from a classification tree on the basis of Change Vector-PCA analysis of the objects. Last, we have an accuracy evaluation according samples which reaches the mapping requirement. The reason of missed or false detection is that some land cover types is significantly affected by the reason, such as farmland, grass. The result shows that Change Vector-PCA based on objects is more superior than the traditional methods based on pixel in terms of utilizing the spatial information and avoiding "salt" noises; and this method can compare several principal components transformed by multi-band difference change information. Change Vector-PCA combines the advantage of CVA and PCA. It is significant in improving the accuracy and automation level for change detection.

Key words: change vector, object-oriented, PCA, change detection