利用多尺度融合进行面向对象的遥感影像变化检测

Object-oriented Change Detection for Remote Sensing Images Based on Multiscale Fusion

在面向对象的变化检测过程中,确定对象的最优分割尺度直接关系到后续的变化信息提取与分析。针对该问题,提出了基于多尺度分割与融合的对象级变化检测新方法。首先,利用由细到粗的尺度分割来获取不同尺寸的目标对象,然后依据对象的特征进行变化向量分析得到各个尺度上的变化检测结果。为了提高变化检测的精度,本文引入模糊融合及两种决策级融合方法进行多尺度融合,并利用SPOT5多光谱遥感图像进行试验。与像素级的变化检测方法相比,总体精度提高了10%左右,试验结果证明了这几种融合策略的有效性和可行性。

In the process of object-oriented change detection,the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis.Aiming at this problem,this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion.First of all,the fine to coarse segmentation is used to obtain initial objects which have different sizes;then,according to the features of the objects,the method of change vector analysis is used to obtain the change detection results of various scales.In order to improve the accuracy of change detection,this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion.Based on these methods,experiments are done with SPOT5multi-spectral remote sensing imagery.Compared with pixel-level change detection methods,the overall accuracy of our method has been improved by nearly 10%,and the experimental results prove the feasibility and effectiveness of the fusion strategies.