超像素分割和多方法融合的遥感影像变化检测方法

Remote Sensing Image Change Detection Algorithm Based on Super-pixel Segmentation and Multiple Difference Maps

针对高空间分辨率的遥感影像,提出了一种结合变化向量分析(CVA)和光谱斜率差异(SGD)的变化检测算法。首先采用基于熵率的方法对影像进行分割,通过改变超像素数目来获取多层次不同尺寸大小的超像素区域,并将分割结果与两时相影像一一对应;接着在光谱空间和斜率空间,提取变化强度影像和斜率差异影像,并对这两种差异信息按照一定的权重进行加权融合;最后结合OTSU阈值分割来获取最终的变化和未变化区域。利用SPOT5多光谱影像进行试验,结果表明这种融合策略可以有效集成不同方法的优势,相较于只利用单一的变化信息,能够提高变化检测过程的稳定性和适用性。

Remote sensing image change detection is widely used in land use,urban expansion,deforestation,as well as disaster assessment etc.This paper presents a novel object-level change detection method for remote sensing images based on the combined utilization of change vector analysis and spectral gradient difference.Firstly,we utilize the entropy rate segmentation method to segment the image and consider the heterogeneity of super-pixels.By changing the number of super-pixels to obtain the multi-layer super-pixel regions with different sizes and the segmentation results are corresponding to the bi-temporal images. Afterwards,in the spectral space and gradient space,we extract the change intensity image and gradient difference image,and make a fusion with these two change information under different weights. At last,the OTSU threshold segmentation algorithm is applied to get the changed/non-changed results. Experimental results on the sets of SPOT5 multi-spectral images show that the fusion strategy can effectively integrate the advantages of the two methods utilized in our process. Compared to using only one single change information,the stability and applicability of the change detection process are improved.