Based on High Resolution of Remote Sensing Data Mining Houses Information Extraction Methods Research

Abstract:

High resolution remote sensing images generally refer to image to the spatial resolution within 10m aerospace、aviation remote sensing images. The emergence of high-resolution images strengthened the ability to recognize the large scale features, especially for the extraction of houses information in mining area. High spatial resolution image has rich delicate texture feature, it is urgent to solution the problem of how to extract the features. The technology is very useful for statistic houses information、village relocation assessment and research of pressure coal status, providing important data basis for village relocation, statistics, assessment. Taking henan as a mining area for example, houses information extraction methods are discussed. This paper mainly research contents as followings: It is combined with the space texture information of high resolution imaging rich, using different methods to extract building information, including followings: First, ordinary image segmentation technology; this method is simple and feasible, but extracted housing information is not accurate. Second, the object-oriented method of feature extraction technology, visualization degree and extracting accuracy of this method is higher; Third, it has conducted the preliminary height extraction of the houses; according to the solar altitude angles and the shadow of the houses to calculate the height of the houses. And considering the influence of undulating terrain, using the terrain DEM data to analyze study area, finally determined the shadow length, and then used solar altitude angles to calculate houses height. Based on the verification, accuracy evaluation results show that houses contour information extraction accuracy is: accuracy of the number and area is over 80%, the total rate of wrong classifications is lower. Houses highly information extraction accuracy is within the 85%. The research methods are effective.

• Periodical: Applied Mechanics and Materials (Volumes 170-173)
• Pages: 2803-2807
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.170-173.2803
• Online since: May 2012
• Authors: Yan Hua Sun, Ping Wang
• Keywords: Division Technology, High Resolution, Information Extraction, Mathematics Morphology, Object-Oriented (OO)

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