An object-oriented change detection method based on multi-feature fusion

Abstract

The invention is applicable to the field of remote sensing technology, and provides an object-oriented change detection method based on multi-feature fusion, comprising the following steps of: S101, image preprocessing; S102, texture feature extraction; S103, image segmentation; S104, feature extraction of that object; S105, generating a difference image; S106, acquire an initial change detectionresult; S107, calculating feature weights; S108, the object change detection result is obtained, and each detected object is clustered into two classes of variable and invariant by fusing multi-dimensional features through weighted fuzzy C-means method. As that embodiment of the invention carry out the processing of the above step on the two-phase images, analyze and determine the weight of each dimension feature, and the weights of each dimension feature are extracted by Relief algorithm, The weighted fuzzy C-means method is formed by adding weights into the fuzzy C-means method, and the weighted fuzzy C-means method is used to fuse the multi-dimensional features, and the detection objects are clustered into two categories: variable and invariant, which effectively fuses the different features to carry out object-oriented change detection and improves the accuracy of the change detection results. 本发明适用于遥感技术领域，提供了一种融合多特征的面向对象变化检测方法，包括S101、影像预处理；S102、纹理特征提取；S103、影像分割；S104、对象特征提取；S105、生成差分影像；S106、初始变化检测结果获取；S107、计算特征权重；S108、得到对象变化检测结果，通过加权模糊C均值法融合多维特征，将各个检测对象聚类为变与不变的两类。本发明实施例通过对两期图像进行上述步骤的处理，分析并确定各维特征的权重，并通过Relief算法提取各维特征的权重，将权重加入模糊C均值法中形成加权模糊C均值法，通过加权模糊C均值法融合多维特征，将各个检测对象聚类为变与不变的两类，有效地融合了不同特征进行面向对象的变化检测，提高了变化检测结果的精度。