期刊
JOURNAL OF SPATIAL SCIENCE
卷 55, 期 1, 页码 117-132出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/14498596.2010.487854
关键词
urban remote sensing; object-based classification; multi-level structure detection; fuzzy logic; decision fusion; transferability
Urban morphology is characterized by a complex and variable coexistence of diverse, spatially and spectrally heterogeneous objects Built-up areas are among the most rapidly changing and expanding elements of the landscape Thus, remote sensing becomes an essential field for up-to-date and area-wide data acquisition, especially in explosively sprawling cities of developing countries The urban heterogeneity requires high spatial resolution image data for an accurate geometric differentiation of the small-scale physical features This study proposes an object-based, multi-level, hierarchical classification framework combining shape, spectral, hierarchical and contextual information for the extraction of in ban features The particular focus is on high class accuracies and stable transferability by fast and easy adjustments on varying urban structures or sensor characteristics The framework is based on a modular concept following a chronological workflow from a bottom-up segmentation optimization to a hierarchical, fuzzy-based decision fusion top-down classification The workflow has been developed on IKONOS data for the megacity Istanbul, Turkey Transferability is tested based on Quickbird data from the various urban structures of the incipient megacity Hyderabad, India The validation of both land-cover classifications shows an overall accuracy of more than 81 percent
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据