Remote Sens. 2017, 9(3), 252; doi:10.3390/rs9030252
Urban Change Analysis with Multi-Sensor Multispectral Imagery
1
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China
3
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing and the Collaborative Innovation Center for Geospatial Technology, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-MartÃnez, Soe W. Myint and Prasad S. Thenkabail
Received: 5 January 2017 / Revised: 6 March 2017 / Accepted: 6 March 2017 / Published: 9 March 2017
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
Abstract
An object-based method is proposed in this paper for change detection in urban areas with multi-sensor multispectral (MS) images. The co-registered bi-temporal images are resampled to match each other. By mapping the segmentation of one image to the other, a change map is generated by characterizing the change probability of image objects based on the proposed change feature analysis. The map is then used to separate the changes from unchanged areas by two threshold selection methods and k-means clustering (k = 2). In order to consider the multi-scale characteristics of ground objects, multi-scale fusion is implemented. The experimental results obtained with QuickBird and IKONOS images show the superiority of the proposed method in detecting urban changes in multi-sensor MS images. View Full-Text
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Tang, Y.; Zhang, L. Urban Change Analysis with Multi-Sensor Multispectral Imagery. Remote Sens. 2017, 9, 252.
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