Affiliation:
1. School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
2. Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Abstract
Instance segmentation has been widely applied in building extraction from remote sensing imagery in recent years, and accurate instance segmentation results are crucial for urban planning, construction and management. However, existing methods for building instance segmentation (BSI) still have room for improvement. To achieve better detection accuracy and superior performance, we introduce a Hybrid Task Cascade (HTC)-based building extraction method, which is more tailored to the characteristics of buildings. As opposed to a cascaded improvement that performs the bounding box and mask branch refinement separately, HTC intertwines them in a joint multilevel process. The experimental results also validate its effectiveness. Our approach achieves better detection accuracy compared to mainstream instance segmentation methods on three different building datasets, yielding outcomes that are more in line with the distinctive characteristics of buildings. Furthermore, we evaluate the effectiveness of each module of the HTC for building extraction and analyze the impact of the detection threshold on the model’s detection accuracy. Finally, we investigate the generalization ability of the proposed model.
Funder
National Natural Science Foundation of China
Guangdong Basic and Applied Basic Research Foundation
Shenzhen Science and Technology Program
Subject
General Earth and Planetary Sciences
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