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Sensors 2017, 17(6), 1295; doi:10.3390/s17061295

Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application

1
College of Information Science and Engineering, Xinjiang University, Urumuqi 830046, China
2
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China
3
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New Zealand
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 24 March 2017 / Revised: 1 June 2017 / Accepted: 1 June 2017 / Published: 6 June 2017
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [4062 KB, uploaded 6 June 2017]   |  

Abstract

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information’s relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection. View Full-Text
Keywords: change detection; nonsubsampled contourlet transform; Hidden Markov Tree model; NSCT-HMT model; FLICM change detection; nonsubsampled contourlet transform; Hidden Markov Tree model; NSCT-HMT model; FLICM
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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, P.; Zhang, Y.; Jia, Z.; Yang, J.; Kasabov, N. Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application. Sensors 2017, 17, 1295.

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