Performance of Unsupervised Change Detection Method Based on PSO and K-means Clustering for SAR Images

• Autorzy: Shehab Jinan N. , Abdulkadhim Hussein A.

Identyfikatory

DOI

10.24425/ijet.2021.137826

• Języki publikacji: EN

Abstrakty

EN

This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time.

Słowa kluczowe

EN

change detection; k-means clustering; multitemporal satellite image; PSO; Gabor wavelet filter; remote sensing

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2021
• Tom: Vol. 67, No. 3
• Strony: 403--408
• Opis fizyczny: Bibliogr. 24 poz., fot., schem., tab., wykr.

Twórcy

autor

Shehab Jinan N.

• University of Diyala, College of Engineering, Dept. of Communication Engineering

autor

Abdulkadhim Hussein A.

• University of Diyala, College of Engineering, Dept. of Communication Engineering

Bibliografia

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