ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 141-147, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-7/141/2016/
doi:10.5194/isprs-annals-III-7-141-2016
 
07 Jun 2016
INTERACTIVE CHANGE DETECTION USING HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON ACTIVE LEARNING WITH GAUSSIAN PROCESSES
Hui Ru1, Huai Yu1, Pingping Huang2, and Wen Yang1 1School of Electronic Information, Wuhan University, 430072 Wuhan, China
2College of Information Engineer, Inner Mongolia University of Technology, 101051 Hohhot, China
Keywords: High Resolution Remote Sensing Images, Change Detection, Active Learning, Gaussian Processes Abstract. Although there have been many studies for change detection, the effective and efficient use of high resolution remote sensing images is still a problem. Conventional supervised methods need lots of annotations to classify the land cover categories and detect their changes. Besides, the training set in supervised methods often has lots of redundant samples without any essential information. In this study, we present a method for interactive change detection using high resolution remote sensing images with active learning to overcome the shortages of existing remote sensing image change detection techniques. In our method, there is no annotation of actual land cover category at the beginning. First, we find a certain number of the most representative objects in unsupervised way. Then, we can detect the change areas from multi-temporal high resolution remote sensing images by active learning with Gaussian processes in an interactive way gradually until the detection results do not change notably. The artificial labelling can be reduced substantially, and a desirable detection result can be obtained in a few iterations. The experiments on Geo-Eye1 and WorldView2 remote sensing images demonstrate the effectiveness and efficiency of our proposed method.
Conference paper (PDF, 1000 KB)


Citation: Ru, H., Yu, H., Huang, P., and Yang, W.: INTERACTIVE CHANGE DETECTION USING HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON ACTIVE LEARNING WITH GAUSSIAN PROCESSES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 141-147, doi:10.5194/isprs-annals-III-7-141-2016, 2016.

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