文章基本信息

标题：Automatic Extraction of Buildings from High Resolution Satellite Images
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作者：M. Ettarid ; M. Rouchdi ; L. Labouab 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII-B8
页码：61-66
出版社：Copernicus Publications
摘要：For obvious economic reasons, buildings and roads are topographic features of great importance for a variety of users. Remote sensing remains a complementary tool when aerial photos reach their limits. When using satellite images two parameters have to be considered: the spatial resolution and the range of spectral channels of the imaging system. Regarding the extraction of buildings and roads, the priority is given to the spatial resolution rather than to the spectral resolution. In this respect, a great improvement of the spatial resolution of the images of the new satellite generation has opened up new perspectives in term of the extraction of precise and global information. However, the pixel-based classification techniques that proved their efficiency on images of medium resolution seems to reach its limits when it comes to high resolution images, due to high heterogeneity that characterizes these images. Therefore, approaches combining spatial and spectral characters are developing.The objective of this paper is to try to implement and experiment an area-based expert classification that draws from the results of a spectral-textural classification. In this approach the research is combining pixel-based classification and area-based classification.In a first stage, we compute for each pixel a set of spatial and spectral parameters, then we assign it to a given class based on these parameters and on probabilistic laws.In the second stage, we will group the pixels within homogeneous regions that will be labeled based on their spectral and geometric characteristics. In fact, at the beginning we apply a spectral-textural classification to the multispectral image. Then, homogeneous regions are extracted using segmentation. The thematic information is hence the first characteristic that will describe regions to be classified using an expert classification. The spatial information is used in the spectral classification and the post classification process as the texture channel.Testing are done on a 2.5m Spot 5 image covering the Beni Amir (Beni Mellal, Morocco) region, and on a QuickBird image (panchromatic and Multispectral) covering the city of Rabat (Morocco). In this research, 11 spatial models were programmed under Erdas using SML language: 8 models describing the texture channels, 2 models to derive spectral indices and 1 model to describe the segments.Qualitative and quantitative evaluation of the results showed that the proposed approach will help improving the results of pixel-based conventional classification
关键词：Remote Sensing; Feature Extraction; Expert Classification; Segmentation