![]() A great amount of spatial and thematic information on land cover at local and national scales is contained in VHR data, and this information clearly gives buildings identifiable shape and texture features. The use of a new generation of very high spatial resolution sensors, such as Ikonos, QuickBird, and Worldview, has broadened the application of remote sensing technology. Due to the high frequency of changes in buildings, understanding their current distribution is important for urban planning, change detection, urban environmental investigations, and urban monitoring applications. In view of both the visual inspection and quantitative assessment, the results of the proposed work are superior to recent automatic building index and supervised binary classification approach results.īuildings are one of the most important types of artificial targets in the urban environment. Three VHR datasets from two satellite sensors, i.e., Worldview-2 and QuickBird, were tested to determine the detection performance. To better detect buildings from the MABI feature image, an object-oriented analysis and building-shadow concurrence relationships were utilized to further filter out non-building land covers, such as roads and bare ground, that are confused for buildings. The dark buildings were considered separately in the MABI to reduce the omission of the dark roofs. Then, the MABI and MASI were calculated by taking the obtained input as a base image. In the pre-processing step of the proposed work, attribute filtering was conducted on the original VHR spectral reflectance data to obtain the input, which has a high homogeneity, and to suppress elongated objects (potential non-buildings). ![]() By investigating the associated attributes in morphological attribute filters (AFs), the proposed method establishes a relationship between AFs and the characteristics of buildings/shadows in VHR images (e.g., high local contrast, internal homogeneity, shape, and size). A new morphological attribute building index (MABI) and shadow index (MASI) are proposed here for automatically extracting building features from very high-resolution (VHR) remote sensing satellite images.
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