Minakshi Kumar, Pradeep Kumar Garg, Sushil Kumar Srivastav, (doi: 10.23953/cloud.ijarsg.338)

Automatic feature extraction from high resolution satellite imagery remains an open research area in the field of remote sensing, computer vision and machine learning. While many algorithms have been proposed for automatic building extraction, none of them solve the problem completely. This paper proposes a system for increasing the degree of automation in extraction of building features from high resolution multispectral satellite images. Image segmentation is a prerequisite for processing of very high spatial resolution imageries. Most image segmentation methods use spectral information of an image alone for generating image objects. A novel image segmentation method for very high spatial resolution multispectral images using combined spectral and structural information is proposed in this paper. The method involves computation of textural parameters from high resolution multispectral imagery and is combined with the spectral bands for extracting spectral-structural characteristics. Hence in addition to the spectral information, the tone, texture and shape information is evaluated for an object-oriented analysis. The support vector machines classification rules are applied on the generated object primitives. The proposed image segmentation method is well applicable to the segmentation of imagery over urban and suburban areas for large scale building extraction.