计算机科学
分割
级联
人工智能
过程(计算)
任务(项目管理)
跳跃式监视
数据挖掘
计算机视觉
机器学习
模式识别(心理学)
系统工程
化学
色谱法
工程类
操作系统
作者
Runqin Deng,Meng Zhou,Yinni Huang,Wei Tu
摘要
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.
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