An Automatic Morphological Attribute Building Extraction Approach for Satellite High Spatial Resolution Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 3; p. 337
• Main Authors: Ma, Weixuan, Wan, Youchuan, Li, Jiayi, Zhu, Sa, Wang, Mingwei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2019
• Subjects: Accuracy; building detection; building index; Buildings; Classification; Feature extraction; High resolution; Homogeneity; Image contrast; Image detection; Image processing; mathematical morphology; morphological attribute filter; morphological profile; Morphology; Pattern recognition; Reflectance; Remote sensing; Researchers; Satellite imagery; Satellites; Shadows; Spatial discrimination; Spatial resolution; Spectral reflectance; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11030337

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. 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). 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). Then, the MABI and MASI were calculated by taking the obtained input as a base image. The dark buildings were considered separately in the MABI to reduce the omission of the dark roofs. 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. Three VHR datasets from two satellite sensors, i.e., Worldview-2 and QuickBird, were tested to determine the detection performance. 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.