结合特征选择的CVA多尺度遥感影像变化检测

针对在多时相变化检测中，面向对象方法无法较好地检测影像中的细微变化，受分割效果以及面向像素方法的影响出现较高虚警率等问题，本文提出了一种结合基于像素的多特征变化向量分析法（CVA）与基于对象的多层次分割的联合判别方法。首先提取不同时相的光谱与纹理特征，利用最大相关最小冗余（mRMR）算法进行特征选择并通过CVA得到像素级变化检测结果；然后对两幅影像进行叠合分割，利用区域合并策略进行不同尺度检测并获取各尺度检测结果；最后结合多种检测结果进行融合，获得最终变化检测结果。检测结果表明本文所提方法能有效降低漏检率，同时提高了检测的准确性。

CVA multi-scale remote sensing image change detection combined with feature selection

Aiming at the problems of the object-oriented method can not detect the subtle changes in the image, the segmentation effect, and the high false alarm rate in the pixel-oriented method in the multi-temporal change detection. In this paper, a combined with multiple characteristics change vector analysis (CVA) method based on pixel and multi-level segmentation joint discriminant method based on the object. Firstly, the spectral and texture features of different time phases are extracted, and the maximum correlation minimum redundancy (mRMR) algorithm is used to select the features and the pixel-level change detection results are obtained by CVA. Then the two images are superimposed and split, and the regional merge strategy is used to make different scale detection, obtain the test results of each scale. And finally combine the multiple test results to determine the final change test results. The experimental results show that the proposed method can effectively reduce the missed detection rate and improve the accuracy of detection.