A multispectral image segmentation approach for object-based image classification of high resolution satellite imagery

Image segmentation has been recognized as an essential process that performs an object-based rather than a pixel-based classification of high-resolution satellite imagery. This paper presents an efficient image segmentation method that considers the spatial and spectral information of high-resolution pan-sharpened imagery. First, we conduct multispectral nonlinear edge preserving smoothing and extract the multispectral edge, which is used as valuable information for seed selection and image segmentation. The initial seeds are automatically selected using the proposed edge variation-based seed selection method, which uses the obtained multispectral edge in a local region. After automatic selection of significant seeds, image segmentation is achieved by applying the modified seeded region growing procedure, which integrates the multispectral and gradient information existing in the image to provide homogenous image regions with accurate and closed boundaries. Experimental results on two multispectral satellite images are given to show that the proposed approach has capability superiority to the previous segmentation techniques on visual evaluation and quantitative comparative assessment.