Unsupervised Remote Sensing Image Change Detection Based on Fusion and IFLICM Algorithm

doi:10.13474/j.cnki.11-2246.2018.0070

Abstract

Abstract:

A new method based on multiscale wavelet fusion and improved unsupervised fuzzy clustering algorithm for multispectral remote sensing image change detection is proposed.This algorithm solves the problems that many state of arts algorithms causing too much noise,and failing to fully exploit the spatial relationship between pixels.Firstly,the difference image is constructed by the method of two-dimensional discrete wavelet transform (DWT) multi-scale decomposition,so the speckle noise is constrained and the changed regions are highlighted by fusing two wavelet decomposition coefficients.Next,Considering the spatial neighborhood information of pixels,the improved fuzzy local information clustering (IFLICM) method is implemented to get the result of the change detection on the basis of fusion image.Experiments on multi-temporal images show that the image fusion strategy integrates the advantages of CVA and ADI images and gains a better performance.The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than other clustering algorithms.

Key words: wavelet transform, multiscale fusion, fuzzy clustering, change detection

CLC Number:

P237

YAN Yu, LIU Yaolin. Unsupervised Remote Sensing Image Change Detection Based on Fusion and IFLICM Algorithm[J]. 测绘通报, 2018, 0(3): 25-31.

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