基于典型相关分析的多元变化检测

Multivariate Change Detection Based on Canonical Transformation

通过对传统变化检测方法存在不足的分析 ,引进典型相关分析的基础理论 ,将不同时相的多通道遥感数据视为分组的多元随机变量 ,利用典型变换进行遥感数据的多元变化检测。实验结果表明 ,所提出的M变换方法用于多时相、多通道遥感影像的变化检测具有明显的优势和应用前景。

The change detection is one of the important topics in multi_temporal remotely sensed data. The present paper introduces a method for multivariate change detection, which is based on the canonical correlation analysis and the orthogonal transformation. Moreover, an experiment with NOAA/AVHRR data is presented.\;Differing from traditional multivariate change detection schemes such as the principal component analysis (PCA), this method takes two multivariate or multi_spectral satellite images as a whole set; each image set (of both) covers the same geographic locations and is typically acquired at different times. Then the two_date image sets are transformed into one set of new random multivariate by using the canonical transformation. By doing so the correlation between the spectral bands in the same image and in the two_date images are removed so that the actual changes in all bands can be simultaneously and accurately detected. This method has been tested for inundation detection of Poyang Lake of China during the summer 1998 flood along Chang Jiang. The results were very promising. The method has a great potential for automatic change detection by using the multi_sensor and multi_temporal remotely sensed data.