Multilevel Tsallis entropy based segmentation for detection of object and shadow in SONAR images

In the proposed work, Multilevel Tsallis entropy is employed to segment side scan SONAR images. Bilevel thresholding detects only the object whereas Multilevel thresholding detects both object and its shadow in a given image. Shadow detection provides more details of the target [1]. In Tsallis entropy method, initially an objective function is evaluated. This function considers the probability density function of the pixels distribution to evaluate the threshold for bilevel and multilevel. Objective function is a function of entropic index `q´ and is varied from 0.1 to 0.9 to determine the threshold value for bilevel. For obtaining multilevel threshold, `q´ is varied from 2 to 9. PSNR is calculated for both bilevel and multilevel to select the optimal threshold. Based on the threshold value, image is segmented and analysed. The results show that Tsallis multilevel thresholding is able to segment the object and the shadow in all the considered images. PSNR values for the considered images are 15 dB, 15.1 dB, 17.9 dB for bilevel 15.8 dB, 17.5 dB and 18 dB for multilevel thresholding.