Abstract
ABSTRACTAutomatic building extraction from High-Resolution Satellite (HRS) image has been an important field of research in the area of remote sensing. Different techniques related to radiometric, geometric, edge detection and object based have already been discussed and used by various researchers for building extraction. However, faithfulness of extraction is highly dependent on user intervention. This study proposes a novel morphological based automatic approach for extraction of buildings using HRS image. Moreover, using such an automatic approach, buildings can be detected having different size and shape. The proposed technique integrates morphological Top-hat filter, and K-means algorithm to extract buildings having bright and dark rooftops. Further, extracted bright and dark rooftop building segments have been combined together to obtain the final output that contains final extracted building segments. In order to eliminate false-detected buildings, different parameters like area, eccentricity, and axis ratio (major/minor axis) have been used. The suitability of the technique has been judged using different indicators such as completeness, correctness and quality.
Highlights
Automatic extraction of buildings has found its applications in various areas like land use land cover mapping, change detection, urban planning, disaster management and many other socio-economic activities
This study proposes a novel morphological based automatic approach for extraction of buildings using High-Resolution Satellite (HRS) image
Automatic building extraction has been an important area of research in remote sensing
Summary
Automatic extraction of buildings has found its applications in various areas like land use land cover mapping, change detection, urban planning, disaster management and many other socio-economic activities. The contrast between roof of building and surrounding region may be low, which has been an important criteria in segmentation and varying roof material reveals different spectral characteristics. Considering all these difficulties, different filters, which have been used to extract edges using satellite imagery, have been broadly classified into three groups, namely, gradient based, laplacian based and morphology based (Katiyar & Arun, 2014). Since, both gradient- and laplacian-based filters are very sensitive to noise, mathematical morphology-based technique has been used in this study to extract buildings using HRS imagery
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