Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images
Change detection (CD) of any surface using multitemporal remote sensing images is an important research topic since up-to-date information about earth surface is of great value. Abrupt changes are occurring in different earth surfaces due to natural disasters or man-made activities which cause damag...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/3123967 |
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| _version_ | 1832567394234531840 |
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| author | Mumu Aktar Md. Al Mamun Md. Ali Hossain |
| author_facet | Mumu Aktar Md. Al Mamun Md. Ali Hossain |
| author_sort | Mumu Aktar |
| collection | DOAJ |
| description | Change detection (CD) of any surface using multitemporal remote sensing images is an important research topic since up-to-date information about earth surface is of great value. Abrupt changes are occurring in different earth surfaces due to natural disasters or man-made activities which cause damage to that place. Therefore, it is necessary to observe the changes for taking necessary steps to recover the subsequent damage. This paper is concerned with this issue and analyzes statistical similarity measure to perform CD using remote sensing images of the same scene taken at two different dates. A variation of normalized mutual information (NMI) as a similarity measure has been developed here using sliding window of different sizes. In sliding window approach, pixels’ local neighborhood plays a significant role in computing the similarity compared to the whole image. Thus the insignificant global characteristics containing noise and sparse samples can be avoided when evaluating the probability density function. Therefore, NMI with different window sizes is proposed here to identify changes using multitemporal data. Experiments have been carried out using two separate multitemporal remote sensing images captured one year apart and one month apart, respectively. Experimental analysis reveals that the proposed technique can detect up to 97.71% of changes which outperforms the traditional approaches. |
| format | Article |
| id | doaj-art-a695c8301cce4234bcae0984aeb27eb2 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-a695c8301cce4234bcae0984aeb27eb22025-02-03T01:01:40ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552017-01-01201710.1155/2017/31239673123967Statistical Similarity Based Change Detection for Multitemporal Remote Sensing ImagesMumu Aktar0Md. Al Mamun1Md. Ali Hossain2Computer Science & Engineering Department, Rajshahi University of Engineering & Technology, Rajshahi 6204, BangladeshComputer Science & Engineering Department, Rajshahi University of Engineering & Technology, Rajshahi 6204, BangladeshComputer Science & Engineering Department, Rajshahi University of Engineering & Technology, Rajshahi 6204, BangladeshChange detection (CD) of any surface using multitemporal remote sensing images is an important research topic since up-to-date information about earth surface is of great value. Abrupt changes are occurring in different earth surfaces due to natural disasters or man-made activities which cause damage to that place. Therefore, it is necessary to observe the changes for taking necessary steps to recover the subsequent damage. This paper is concerned with this issue and analyzes statistical similarity measure to perform CD using remote sensing images of the same scene taken at two different dates. A variation of normalized mutual information (NMI) as a similarity measure has been developed here using sliding window of different sizes. In sliding window approach, pixels’ local neighborhood plays a significant role in computing the similarity compared to the whole image. Thus the insignificant global characteristics containing noise and sparse samples can be avoided when evaluating the probability density function. Therefore, NMI with different window sizes is proposed here to identify changes using multitemporal data. Experiments have been carried out using two separate multitemporal remote sensing images captured one year apart and one month apart, respectively. Experimental analysis reveals that the proposed technique can detect up to 97.71% of changes which outperforms the traditional approaches.http://dx.doi.org/10.1155/2017/3123967 |
| spellingShingle | Mumu Aktar Md. Al Mamun Md. Ali Hossain Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images Journal of Electrical and Computer Engineering |
| title | Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images |
| title_full | Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images |
| title_fullStr | Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images |
| title_full_unstemmed | Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images |
| title_short | Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images |
| title_sort | statistical similarity based change detection for multitemporal remote sensing images |
| url | http://dx.doi.org/10.1155/2017/3123967 |
| work_keys_str_mv | AT mumuaktar statisticalsimilaritybasedchangedetectionformultitemporalremotesensingimages AT mdalmamun statisticalsimilaritybasedchangedetectionformultitemporalremotesensingimages AT mdalihossain statisticalsimilaritybasedchangedetectionformultitemporalremotesensingimages |