A single-building damage detection model based on multi-feature fusion: A case study in Yangbi.

ABSTRACT: Accurate and effective identification, determination of the location, and classification of damaged buildings are essential after destructive earthquakes. However, the accuracy of image change detection is limited because of the many texture features and changes in non-building information. In this context, a model for single-building damage detection based on multi-feature fusion is proposed. First, the normalized Digital Surface Model (nDSM) was extracted from the DSM through iterative filtering and point cloud thinning, followed by the extraction of building contour information. Next, single-building images were generated from different data sources through the region of interest (ROI), and the optimal texture feature parameters were extracted for fusion. Afterward, principal-component

SUBMITTER: Du H 

PROVIDER: S-EPMC10758967 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature