CHANGE DETECTION BY FUSING ADVANTAGES OF THRESHOLD AND CLUSTERING METHODS
M. Tan, M. Hao
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences · 2017
doi:10.5194/isprs-archives-xlii-2-w7-897-2017 (opens in a new tab)
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Abstract
In change detection (CD) of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximum (EM) algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM) obtains a homogeneous CD map but with some missed detections. Therefore, we aim to design a framework to improve CD results by fusing the advantages of threshold and clustering methods. Experimental results indicate the effectiveness of the proposed method.
Abstract licence: CC BY
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