Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 897-901, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-897-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 897-901, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-897-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

CHANGE DETECTION BY FUSING ADVANTAGES OF THRESHOLD AND CLUSTERING METHODS

M. Tan and M. Hao M. Tan and M. Hao
  • School of Environment and Spatial Informatics, China University of Mining and Technology, 221116 Xuzhou City, China

Keywords: Change Detection, Medium Resolution, Remote Sensing, Threshold, Clustering, Advantage Fusion

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.