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Settlement extraction in the North China Plain using Landsat and Beijing-1 multispectral data with an improved watershed segmentation algorithm

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Abstract:

In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30-40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM + ) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30-32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160903475332

Affiliations: 1: State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, PR, China 2: State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, PR, China,Division of Ecosystem Sciences, University of California, Berkeley, CA, USA 3: State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing, PR China 4: College of Global Change and Earth System Science, Beijing Normal University, Beijing, PR China 5: Beijing Landview Mapping Information Technology Co., Beijing, PR China

Publication date: February 1, 2010

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