Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images
Joint Authors
Aktar, Mumu
Mamun, Md. Al
Hossain, Md. Ali
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-24
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Aktar, Mumu& Mamun, Md. Al& Hossain, Md. Ali. 2017. Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images. Journal of Electrical and Computer Engineering،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1175260
Modern Language Association (MLA)
Aktar, Mumu…[et al.]. Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images. Journal of Electrical and Computer Engineering No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1175260
American Medical Association (AMA)
Aktar, Mumu& Mamun, Md. Al& Hossain, Md. Ali. Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images. Journal of Electrical and Computer Engineering. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1175260
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175260