一种适用于粗匹配的遥感影像快速变化发现方法

针对遥感影像变化检测过程中影像间相对配准精度对检测结果影响大，易产生细碎图斑和错检冗余等问题，本文提出了一种基于感知哈希算法适用于粗匹配的遥感影像变化发现方法，以待检测影像的光谱信息和纹理特征信息共同作为检测特征集，以分割后的格网作为检测单元，采用基于感知哈希算法的影像相似度比对方法，计算格网内图像相似度，提取变化位置。试验结果表明，采用该方法可有效减少变化发现对影像间相对配准精度的依赖性，在本文试验中，相对配准精度在15像元以内均可保证95%以上的提取准确率和5%以下的漏检率，对配准精度具有良好的稳健性。

A change detection method for low-precision registration remote sensing images

Traditional methods of change detection for remote sensing images are highly relied on high-precision relative-registration,and mostly cost many broken pieces of polygons which are redundantly or wrongly extracted. In this paper,a rapid low-precision registration dependent method for remote sensing images changing detection is extracted. The method is based on the Perceptual Hash algorithm,which uses both the spectral and texture features as the detection feature sets,makes grid partition of the images as the detection object,calculates the similarity-index of each pair of the grid using the Perceptual Hash algorithm to extract the changing area. Result shows that,the method makes effective promotion of reducing the dependent of the relative-registration precision. The accuracy of correct-extraction can stay 95%,and the incorrect-extraction rate is lower than 5%,with a registration-shift within 15 pixels,which is robust for the registration precision.