基于压缩数据维的城市建筑用地遥感信息提取

通过压缩数据维的方式，研究城市建筑用地信息准确提取的原理和方法。通过对城市土地利用类型的分析，选取了归一化差异建筑指数、修正归一化差异水体指数和土壤调节植被指数来代表城市建成区的3种最主要地类——建筑用地、水体和植被。通过将ETM 影像原有的7个波段压缩为由它们衍生的这3个采用比值运算构成的指数波段，大大压缩了数据维数、减少了数据的相关度并降低了不同地类的光谱混淆性。因此采用简单的最大似然分类和掩膜处理技术，就可以将城市建筑用地信息提取出来，其精度可达91．2％。

Remote Sensing Information Extraction of Urban Built up Land Based on a Data dimension Compression Technique

Based on a data dimension compression technique, this paper studies the principles and method of remote sensing information extraction for urban built up land. With the detailed analysis of urban land use types, the study selects three indices,i.e. 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, including built up land, water body, and vegetation. This reduced 7 multispectral bands of a Landsat 7 ETM+ subscene of Fuzhou city to three index bands generated from the original multispectral bands and thus dramatically decreased band correlation, data redundancy and spectral confusion between different land use/cover classes. The three index bands are then used to compose a new image. A maximum likelihood based supervised classification was carried out on the new three band image and the built up land is finally extracted by masking out non built up land classes. The extraction result achieves a 91.2% overall accuracy. Therefore, the method is an effective one for the remote sensing information extraction of urban land use.