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Remote Sens. 2019, 11(3), 359; https://doi.org/10.3390/rs11030359

Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis

1,2,*,†
,
2,†
,
2
and
2,*
1
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
2
Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 24 December 2018 / Revised: 19 January 2019 / Accepted: 1 February 2019 / Published: 11 February 2019
(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)
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Abstract

The drawback of pixel-based change detection is that it neglects the spatial correlation with neighboring pixels and has a high commission ratio. In contrast, object-based change detection (OBCD) depends on the accuracy of the segmentation scale, which is of great significance in image analysis. Accordingly, an object-based approach for automatic change detection using multiple classifiers and multi-scale uncertainty analysis (OB-MMUA) in high-resolution (HR) remote sensing images is proposed in this paper. In this algorithm, the gray-level co-occurrence matrix (GLCM), morphological, and Gabor filter texture features are extracted to construct the input data, along with the spectral features, to utilize the respective advantages of the features and to compensate for the insufficient spectral information. In addition, random forest is used to select the features and determine the optimal feature vectors for the change detection. Change vector analysis (CVA) based on uncertainty analysis is then implemented to select the initial training samples. According to the diversity, support vector machine (SVM), k-nearest neighbor (KNN), and extra-trees (ExT) classifiers are then chosen as the base classifiers for Dempster-Shafer (D-S) evidence theory fusion, and unlabeled samples are selected using an active learning method with spatial information. Finally, multi-scale object-based D-S evidence theory fusion and uncertainty analysis is used to classify the difference image. To validate the proposed approach, we conducted experiments using multispectral images collected by the ZY-3 and GF-2 satellites. The experimental results confirmed the effectiveness and superiority of the proposed approach, which integrates the respective advantages of the pixel-based and object-based methods. View Full-Text
Keywords: change detection; multi-feature; ensemble classifiers; multi-scale uncertainty analysis change detection; multi-feature; ensemble classifiers; multi-scale uncertainty 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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Tan, K.; Zhang, Y.; Wang, X.; Chen, Y. Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis. Remote Sens. 2019, 11, 359.

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