Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application
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 de...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/17/6/1295 |
| _version_ | 1798034967823908864 |
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| author | Pengyun Chen Yichen Zhang Zhenhong Jia Jie Yang Nikola Kasabov |
| author_facet | Pengyun Chen Yichen Zhang Zhenhong Jia Jie Yang Nikola Kasabov |
| author_sort | Pengyun Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:51:43Z |
| format | Article |
| id | doaj.art-2062289d12704b04a06e41b1eef72633 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:51:43Z |
| publishDate | 2017-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2062289d12704b04a06e41b1eef726332022-12-22T04:03:49ZengMDPI AGSensors1424-82202017-06-01176129510.3390/s17061295s17061295Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its ApplicationPengyun Chen0Yichen Zhang1Zhenhong Jia2Jie Yang3Nikola Kasabov4College of Information Science and Engineering, Xinjiang University, Urumuqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumuqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumuqi 830046, ChinaInstitute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, ChinaKnowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New ZealandTraditional 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.http://www.mdpi.com/1424-8220/17/6/1295change detectionnonsubsampled contourlet transformHidden Markov Tree modelNSCT-HMT modelFLICM |
| spellingShingle | Pengyun Chen Yichen Zhang Zhenhong Jia Jie Yang Nikola Kasabov Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application Sensors change detection nonsubsampled contourlet transform Hidden Markov Tree model NSCT-HMT model FLICM |
| title | Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application |
| title_full | Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application |
| title_fullStr | Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application |
| title_full_unstemmed | Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application |
| title_short | Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application |
| title_sort | remote sensing image change detection based on nsct hmt model and its application |
| topic | change detection nonsubsampled contourlet transform Hidden Markov Tree model NSCT-HMT model FLICM |
| url | http://www.mdpi.com/1424-8220/17/6/1295 |
| work_keys_str_mv | AT pengyunchen remotesensingimagechangedetectionbasedonnscthmtmodelanditsapplication AT yichenzhang remotesensingimagechangedetectionbasedonnscthmtmodelanditsapplication AT zhenhongjia remotesensingimagechangedetectionbasedonnscthmtmodelanditsapplication AT jieyang remotesensingimagechangedetectionbasedonnscthmtmodelanditsapplication AT nikolakasabov remotesensingimagechangedetectionbasedonnscthmtmodelanditsapplication |