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Accurate building imaging from VHR imagery using generalized differential morphological attribute profile

Abstract Differential morphological profile (DMP) and differential attribute profile (DAP) are effective tools for automated building extraction from very high-resolution (VHR) images. Compared to the DMP, DAP provides a more flexible way to model image features not only related to the scale of regions, but also any measure (structural, spatial, and spectral) that can be evaluated in the region. The DAP series can be regarded as a structural spectrum that depicts the responses of the image components related to different attributes and scales. However, the conventional DAP-based building feature extraction approach ignores discriminative information for features that are across the scales in the attribute profiles. In this study, an improvement in the discriminatory power of DAP for building feature extraction is proposed. To obtain the entire differential profiles, a scale-span difference attribute profile called generalized DAP (GDAP) is presented, based on which, a new generalized morphological attribute-building index is proposed for the automatic extraction of buildings from VHR imagery. The GDAP can describe the complete attribute spectrum and measure the difference between arbitrary scales, which is more appropriate for representing the multiscale characteristics of buildings. The performance of the proposed method was validated using VHR datasets from an aerial platform. In terms of statistical precision and visual inspection, the results of the proposed framework are superior to those of the relevant supervised and unsupervised building extraction approaches.