An automatic region-based image segmentation algorithm for remote sensing applications
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- An automatic region-based image segmentation algorithm for remote sensing applications
- Wang, Zhongwu; Jensen, John R.; Im, Jungho
- Image segmentation; K-means clustering; Object-based classification; Region growing; Region merging
- Issue Date
- ELSEVIER SCI LTD
- ENVIRONMENTAL MODELLING & SOFTWARE, v.25, no.10, pp.1149 - 1165
- 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.
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