Certain investigations on image segmentation algorithms on synthetic aperture radar images and classification using convolution neural network
G. Srinitya, D. Sharmila, S Logeswari, Daniel Madan Raja S · Concurrency and Computation Practice and Experience · 2021
Abstract Synthetic aperture radar (SAR) image segmentation is the most important step in detecting the land and sea areas in many applications like military in particular. This article experiments four prominent image segmentation algorithms on SAR images and we propose a hybrid method to segment SAR images, which shows better efficiency. First among them is OTSU thresholding method that segmented the image with less interest in the texture component. Further, adaptive thresholding, artificial bee colony segmentation, and wavelet transforms were experimented; wavelet transforms give better segmentation results compared to thresholding methods. In this article, wavelet along with masking threshold including segmentation method is proposed, which gives a better result based on texture and executes in almost the same time as other methods and the segmented images give better classification results using convolution neural network when compared to other methods.