An automatic region-based image segmentation algorithm for remote sensing applications

Authors:
Zhongwu Wang;John R. Jensen;Jungho Im
Affiliations:
The Sanborn Map Company, 320 Miller Ave., Suite 80, Ann Arbor, MI 48103, USA;Department of Geography, University of South Carolina, Columbia, SC 29208, USA;Department of Environmental Resources and Forest Engineering, State University of New York, College of Environmental Science and Forestry, Syracuse, NY 13210, USA
Venue:
Environmental Modelling & Software
Year:
2010

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Abstract

Object-based image analysis has proven its potentials for remote sensing applications, especially when using high-spatial resolution data. One of the first steps of object-based image analysis is to generate homogeneous regions from a pixel-based image, which is typically called the image segmentation process. This paper introduces a new automatic Region-based Image Segmentation Algorithm based on k-means clustering (RISA), specifically designed for remote sensing applications. The algorithm includes five steps: k-means clustering, segment initialization, seed generation, region growing, and region merging. RISA was evaluated using a case study focusing on land-cover classification for two sites: an agricultural area in the Republic of South Africa and a residential area in Fresno, CA. High spatial resolution SPOT 5 and QuickBird satellite imagery were used in the case study. RISA generated highly homogeneous regions based on visual inspection. The land-cover classification using the RISA-derived image segments resulted in higher accuracy than the classifications using the image segments derived from the Definiens software (eCognition) and original image pixels in combination with a minimum-distance classifier. Quantitative segmentation quality assessment using two object metrics showed RISA-derived segments successfully represented the reference objects.