Skip to main content

Index Geophysics

Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering

Item

Title (Dublin Core)

Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering

Description (Dublin Core)

In the present study, an improved iteratively reweighted multivariate alteration detection (IR-MAD) algorithm was proposed to improve the contribution of weakly correlated bands in multi-spectral image change detection. In the proposed algorithm, each image band was given a different weight through single-band iterative weighting, improving the correlation between each pair of bands. This method was used to obtain the characteristic difference in the diagrams of the band that contain more variation information. After removing Gaussian noise from each feature-difference graph, the difference graphs of each band were fused into a change-intensity graph using the Euclidean distance formula. Finally, unsupervised fuzzy C-means (FCM) clustering was used to perform binary clustering on the fused difference graphs to obtain the change detection results. By comparing the original multivariate alteration detection (MAD) algorithm, the IR-MAD algorithm and the proposed IR-MAD algorithm, which used a mask to eliminate strong changes, the experimental results revealed that the multi-spectral change detection results of the proposed algorithm are closer to the actual value and had higher detection accuracy than the other algorithms.

Creator (Dublin Core)

Liyuan Ma
Jia Zhenhong
Jie Yang
Nikola Kasabov

Subject (Dublin Core)

single-band iterative weighting
multi-spectral change detection
ir-mad
fcm clustering
Oceanography
GC1-1581
Geology
QE1-996.5

Publisher (Dublin Core)

Taylor & Francis Group

Date (Dublin Core)

2020-01-01T00:00:00Z

Type (Dublin Core)

article

Identifier (Dublin Core)

2279-7254
10.1080/22797254.2019.1707124
https://doaj.org/article/3444351773a8426a9cd79b9674c275b5

Source (Dublin Core)

European Journal of Remote Sensing, Vol 53, Iss 1, Pp 1-13 (2020)

Language (Dublin Core)

EN

Relation (Dublin Core)

http://dx.doi.org/10.1080/22797254.2019.1707124
https://doaj.org/toc/2279-7254

Provenance (Dublin Core)

Journal Licence: CC BY-NC