Change Detection in Sarimages Based on Artificial Bee Colony Optimization With fuzzy C – Means Clustering
J. Thrisul Kumar1, Y. Mallikarjuna Reddy2, B. Prabhakara Rao3

1J. Thrisul kumar, Research Scholar, Jawaharlal Nehru Technological University, Kakinada (A.P), India.
2Dr. Y. Mallikarjuna Reddy, Principal and Professor, Vasireddy Venkatadri Institute of Technology, Namburu (A.P), India.
3Dr. B. Prabhakara Rao, Professor, Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Kakinada (A.P), India.

Manuscript received on 24 September 2018 | Revised Manuscript received on 30 September 2018 | Manuscript published on 30 November 2018 | PP: 156-160 | Volume-7 Issue-4, November 2018 | Retrieval Number: E1819017519©BEIESP
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Abstract: Synthetic aperture radar (SAR)generates images with high resolution in all weather conditions for a given application. An Artificial Bee Colony (ABC), optimization algorithm is proposed to detect changes in multitemporal SAR images which are captured at same area in various times. It is well- known fact that the speckle noiseis existed in SAR images.In order to reduce the speckle noise in the co-registered images, a novelAdaptive Median filter is implemented in this paper. Afterthe minimization of speckle noise, discrete wavelet (DWT) fusion is exploited for further image segmentation. Also, an Artificial Bee Colony (ABC) optimization technique is adopted for effective smoothing the image to make decisiveimage classification. Using fuzzy c -means clustering classificationwe can detect changed pixels and unchanged pixels.Finally, theresults are comparedwith DWT-FCM (without optimization), GeneticAlgorithm (GA)optimizationand proposed ABC optimizationAlgorithm. The performance of proposed technique iscomparedin terms ofaccuracy, sensitivity, precision and F1 – score.
Keywords: SAR, Optimization, ABC algorithm, GA algorithm and Fuzzy – C means clustering.

Scope of the Article: Artificial Intelligence and Machine Learning