Abstract
Remote sensing change detection (CD) plays an important role in Earth observation. In this paper, we propose a novel fusion approach for unsupervised CD of multispectral remote sensing images, by introducing majority voting (MV) into fuzzy topological space (FTMV). The proposed FTMV approach consists of three principal stages: (1) the CD results of different difference images produced by the fuzzy C-means algorithm are combined using a modified MV, and an initial fusion CD map is obtained; (2) by using fuzzy topology theory, the initial fusion CD map is automatically partitioned into two parts: a weakly conflicting part and strongly conflicting part; (3) the weakly conflicting pixels that possess little or no conflict are assigned to the current class, while the pixel patterns with strong conflicts often misclassified are relabeled using the supported connectivity of fuzzy topology. FTMV can integrate the merits of different CD results and largely solve the conflicting problem during fusion. Experimental results on three real remote sensing images confirm the effectiveness and efficiency of the proposed method.
Highlights
IntroductionRemote sensing change detection (CD) is a powerful process used to identify the changes on the Earth’s surface using remote sensing images acquired of the same scene at different times
To illustrate the difference between the change detection (CD) maps and the corresponding reference image clearly, each CD map was divided into four parts: black and white represent the correctly detected no-change and change pixels, whereas red and yellow denote the missed detections (MD) and false alarms (FA)
An initial fusion CD map is first produced by combining the CD results from different difference image (DI) images using an improved majority voting (MV) (i.e., fuzzy MV (FMV))
Summary
Remote sensing change detection (CD) is a powerful process used to identify the changes on the Earth’s surface using remote sensing images acquired of the same scene at different times. Such a process plays an important role in various fields, such as disaster assessment, urban studies, and environmental monitoring. A lot of CD methods have been designed and proposed in past decades [1]. These methods can be roughly divided into supervised and unsupervised groups, based on whether training samples are required [2]
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