文章基本信息

标题：Landsat image-based LULC changes of San Antonio, Texas, using advanced atmospheric correction and object-oriented image analysis approaches
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作者：A. Owojori ; H. Xie
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：2005
卷号：XXXVI-8/W27
出版社：Copernicus Publications
摘要：The City of San Antonio, Texas and its surrounding have been experiencing rapid land-use and land-cover (LULC) changes as a result of population growth and urban development in the last few decades. Current census data shows that San Antonio is the 8 th largest city in the United States, surpassing Dallas and Detroit (U.S. Census Bureau). With such population increase comes increased pressure on the environment and natural resources. Multitemporal datasets consisting of Landsat TM images of 1985 and 2003 were used to perform change detection analysis, with the aim of achieving LULC characterization and pattern change between the two periods. Data pre-processing includes the use of Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH TM ) for atmospheric correction. Image classification was performed using the object-oriented approach (eCognition TM ). Change analysis shows that impervious surface (or urban area) increased in the study area by 33% between 1985 and 2003. The expected corresponding decrease in forest cover, even though observable especially within and around the city limits, is not reflected in the overall change information. The reasons for this are discussed in detail. While many subjective factors affect the classification accuracy using pixel-based supervised image classification, the work suggests that eCognition-based object-oriented classification is also affected by subjective factors such as the weight of shape (texture) and pixel value, levels of multiresolution segmentation, and selection of objects as training sites
关键词：San Antonio; Object-oriented image classification; Atmospheric correction; Change detection