Three Phase Segmentation Algorithm for High Resolution Satellite Images

To segment multi-spectral remote sensor images, feature extraction and object classification is an essential step that performs region-based segmentation instead of a pixel-based segmentation. Spectral based segmentation methods like K-Means, Mean-shift segmentation fail to extract optimal regions from multi-spectral images. In high-resolution multi-spectral images, segmentation main aim is to divide the image into set of non overlapping regions based on spatial features. In this proposed scheme, three phases are used to segment the remote sensing images. In the first phase, remote sensing image is divided into spatial blocks by applying the filter method. After the preprocessing step, watershed segmentation method is applied to get the initial segmented marked regions. In the second phase, noisy segmented regions are identified and then eliminated using statistical threshold method. In the third phase, area based reduced segmentation method is proposed to reduce the number of segmented regions. Experimental result shows the proposed approach has better performance compared to the traditional segmentation techniques in terms of time, noise and over segmentation.