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Remote Sens. 2016, 8(7), 549; doi:10.3390/rs8070549

Multi-Feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis

1,2
and
1,2,*
1
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2
College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editors: Norman Kerle and Prasad S. Thenkabail
Received: 27 April 2016 / Revised: 21 June 2016 / Accepted: 24 June 2016 / Published: 28 June 2016
View Full-Text   |   Download PDF [11852 KB, uploaded 28 June 2016]   |  

Abstract

Change detection in multi-temporal remote sensing images has usually been treated as a problem of explicitly detecting land cover transitions. To date, multi-dimensional change vector analysis has been an effective solution to such problems. However, using change vector analysis makes it hard to calculate multiple directions or kinds of change. Through combining multi-feature object-based image analysis and change vector analysis, this paper presents a novel method for object-based change detection of multiple changes. Our technique, named self-adaptive weight-change vector analysis, carries out: (1) change vector analysis to determine magnitude and direction of changes; and (2) self-adaptive weight-based analysis of the standard deviation of image objects. Furthermore, a polar representation has been adopted to acquire visual change information for image objects. This paper proposes an automatic technique that can be applied to the field of multi-feature object-based change detection for very high resolution remotely sensed images. The two-step automatic detection strategy includes extraction of changed objects using an expectation-maximization algorithm to estimate the threshold under a Gaussian assumption, and identification of different kinds of changes using a K-means clustering algorithm. The effectiveness of our approach has been tested on both multispectral and panchromatic fusion images. Results of these two experimental cases confirm that this approach can detect multiple kinds of change. We found that self-adaptive weight-change vector analysis had superior capabilities of object-based change detection compared with standard change vector analysis, yielding Kappa statistics of 0.7976 and 0.7508 for Cases 1 and 2, respectively. View Full-Text
Keywords: change vector analysis; object-based change detection; self-adaptive weights; multi-dimensional change; remote sensing; image analysis change vector analysis; object-based change detection; self-adaptive weights; multi-dimensional change; remote sensing; image analysis
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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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Chen, Q.; Chen, Y. Multi-Feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis. Remote Sens. 2016, 8, 549.

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