1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; 2. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
Abstract：Data field can describe the correlation between data objects, and is a simulation of interaction between particles in physical field. Potential value of a data object in data field can effectively represent the spatial interactions of its neighborhoods, and it can do so for pixels in high resolution remote-sensing image. In this paper, the authors propose a method for residential area extraction from high resolution remote-sensing image using data field and density clustering. The major steps are as follows: the generating of a high resolution remote-sensing image data field; the calculation if potential value for each pixel in this field to obtain a new feature image; the segmentation of the feature image via watershed segmentation, and the calculation of centroids of segmentation results; the clustering of all the centroids into different clusters based on the density, with the extracted residential area composed of target clusters. Compared with existing relative methods of residential areas extraction for high resolution remote-sensing images, the experimental results suggest that the presented method is robust and efficient.
岳梦雪, 秦昆, 张恩兵, 张晔, 曾诚. 基于数据场和密度聚类的高分辨率影像居民区提取[J]. 国土资源遥感, 2017, 29(3): 92-97.
YUE Mengxue, QIN Kun, ZHANG Enbing, ZHANG Ye, ZENG Cheng. Residential area extraction for high resolution remote sensing image based on data field and density clustering. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 92-97.
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2017, Vol. 29 
