SAR IMAGE SEGMENTATION USING ABC ALGORITHM
Anusha Gorantla, Gauri Pundlik · IJITR International Journal of Innovative Technology and Research - IJITR International Journal of Innovative Technology and Research · 2016
Conventional methods for segmentation on Synthetic Aperture Radar (SAR) images aim to provide automatic analysis and interpretation of data. Usually the task gets failed at many cases due to the influence of speckle in the segmented results. Due to the presence of speckle noise, segmentation of synthetic aperture radar (SAR) image is still a challenging problem. Artificial Bee Colony algorithm using fitness function. This paper proposes a fast SAR image segmentation method based on Artificial Bee Colony (ABC) algorithm. In this method, threshold estimation is regarded as a search procedure that searches for an appropriate value in a continuous grayscale interval. Hence, ABC algorithm is introduced to search for the optimal threshold. In order to get an efficient fitness function for ABC algorithm, after the definition of grey number in Grey theory, the original image is decomposed by discrete wavelet transform. Then, a filtered image is produced by performing a noise reduction to the approximation image reconstructed with low-frequency coefficients. At the same time, a gradient image is reconstructed with some high-frequency coefficients. A co-occurrence matrix based on the filtered image and the gradient image is therefore constructed, and improved two-dimensional grey entropy is defined to serve as the fitness function of ABC algorithm.