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首页> 外文期刊>Journal of Spatial Science >Object-based feature extraction using high spatial resolution satellite data of urban areas
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Object-based feature extraction using high spatial resolution satellite data of urban areas

机译：利用城市区域的高分辨率卫星数据进行基于对象的特征提取

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摘要

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 urban 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.View full textDownload full textKeywordsurban remote sensing, object-based classification, multi-level structure detection, fuzzy logic, decision fusion, transferabilityRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/14498596.2010.487854
机译：城市形态的特征是不同的，空间的和光谱上异质的对象复杂且可变地共存。建成区是景观中变化最快和扩展最快的区域之一。因此，遥感已成为获取最新和整个区域数据的必不可少的领域，尤其是在发展中国家爆炸性蔓延的城市中。城市异质性需要高空间分辨率的图像数据，才能对小规模物理特征进行精确的几何区分。这项研究提出了一个基于对象的多层次分层分类框架，该框架结合了形状，光谱，分层和上下文信息，以提取城市特征。通过快速轻松地调整变化的城市结构或传感器特性，特别关注高精确度和稳定的可传递性。该框架基于模块化的概念，遵循从下至上的细分优化到基于层次的，基于模糊的决策融合自上而下分类的时间顺序工作流程。该工作流程是基于IKONOS数据开发的，用于土耳其伊斯坦布尔的大城市。可迁移性是根据来自印度大城市海得拉巴各个城市结构的Quickbird数据进行测试的。两种土地覆被分类的验证均显示总体准确率超过81％。查看全文下载全文关键词surban遥感，基于对象的分类，多级结构检测，模糊逻辑，决策融合，可转移性相关var addthis_config = {ui_cobrand： “泰勒和弗朗西斯在线”，services_compact：“ citeulike，netvibes，twitter，technorati，delicious，linkedin，facebook，stumbleupon，digg，google，更多”，发布：“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/14498596.2010.487854

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