%0 Journal Article
%T Fast information extraction of urban built-up land based on the analysis of spectral signature and normalized difference index<br>基于谱间特征和归一化指数分析的城市建筑用地信息提取
%A XU Han-qiu
%A <br>徐涵秋
%J 地理研究
%D 2005
%I 
%X This paper studies the principles and method of fast remote-sensing information extraction for urban built-up land, taking Fuzhou city as an example. With the detailed analysis and clarity of several existing normalized difference indices, the study selects three indices, Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI) and Soil Adjusted Vegetation Index (SAVI) to represent three major urban land use/cover classes, i. e. , built-up land, water body and vegetation, respectively. The three index images are generated from a Landsat ETM subscene of Fuzhou city and then used as three bands to compose a new image. This dramatically compresses the original eight-band ETM image into a three-band image, reduces band correlations and data redundancy, thus significantly simplifiying the band spectral analysisprocedures. The spectral signature analysis only needs to be performed on this three-index composite image and the signature differences among the three major urban land use/cover classes are revealed much easier than being done with multi-bands. Based on the revealed signature differences, the built-up land is finally extracted through a simple logic calculation. The result achieves a 91.3% overall accuracy. Therefore, the method is a fast and accurate one for the remote-sensing information extraction of urban land use without human interference. In addition to the above built-up land information extraction study, the paper proposes a Modified Normalized Difference Water Index (MNDWI) based on the NDWI of Mcfeeters (1996) , which uses MIR wavelength (ETM band 5) instead of NIR wavelength to construct the index. The replacement largely enhances the contrast of the water bodies with the other land use/cover classes and reduces the spectral confusion with the other classes. Therefore, the MNDWI is more suitable for delineating features of polluted urban rivers/lakes. The advantages of using SAVI instead of NDVI in the urban study are also discussed in this paper.
%K urban built-up land
%K remote sensing information extraction
%K spectral signature analysis
%K NDBI
%K SAVI
%K MNDWI<br>城市建筑用地
%K 遥感信息提取
%K 光谱分析
%K NDBI
%K SAVI
%K MNDWI
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=869B153A4C6B5B85&jid=C0C75E88BA2EE501C8298896F64A711F&aid=72EB48361F232A8B&yid=2DD7160C83D0ACED&vid=B91E8C6D6FE990DB&iid=0B39A22176CE99FB&sid=A2745AA1110798CA&eid=CA9ED1AB4D9E3E04&journal_id=1000-0585&journal_name=地理研究&referenced_num=26&reference_num=23