Object based building footprint detection from high resolution multispectral satellite image using K-means clustering algorithm and shape parameters. Issue 6 (12th May 2019)

• Record Type: Journal Article
• Authors: Gavankar, Nitin Laxmanrao; Ghosh, Sanjay Kumar
• Is Part Of: Geocarto international. Volume 34:Issue 6(2019)
• Page Start: 626
• Page End: 643
• Publication Date: 2019-05-12
• Subjects: Object based -- segmentation -- classification -- K-means algorithm -- high resolution data; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2018.1425736
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Object-based image analysis (OBIA) has been a new area of research in satellite image processing applications, since it improves the quality of information acquisition about geospatial objects and also enables to add spatial and contextual information to the objects of interest. The extraction of buildings from High Resolution Satellite (HRS) image in an urban scenario has been an intricate problem due to their different size, shape, varying rooftop textures and low contrast between building and surrounding region. In this study, a new object-based automatic building extraction technique has been proposed to extract building footprints from HRS pan sharpened IKONOS multispectral image. The study is mainly emphasizing on obtaining optimal values for segmentation parameters, shape parameters, and defining rule set to extract buildings and eliminate misclassified other urban features. The suitability of the technique has been judged using different indicators, such as, completeness, correctness and quality.