计算机科学
图像(数学)
土地利用
相(物质)
人工智能
计算机视觉
环境科学
地图学
遥感
地理
土木工程
工程类
化学
有机化学
作者
Kun Zhao,Yu Tian,Lijian Zhou,Tingyuan Nie,Siyuan Hao
标识
DOI:10.1080/2150704x.2022.2113475
摘要
Street view image (SVI) is becoming one of the most essential proximity sensing data for urban land-use study. Because of the highly abstract nature of their labels (e.g., commercial area), straight usage of end-to-end visual models often perform poorly. Recently proposed ‘bottom-up and top-down’ framework has achieved remarkable performance, which transforms visual classification task into text sequence classification task. However, in the ‘top-down’ phase, the long-distance dependence of text information still exists. On the other hand, in the ‘bottom-up’ phase, better detectors are also needed to further extract visual features. In this letter, the idea of ‘feature adaptive weighting’ (FAW), which was derived from the attention mechanism, is used in both phases to improve the overall performance. ‘Self-correlation guided feature adaptive weighting’ (S-FAW) is introduced in the first phase to improve building detection. In the second phase, ‘cross-correlation guided feature adaptive weighting’ (C-FAW) is used to enhance the connections between detected individual buildings. Experimental results show that the proposed FAWNet can effectively improve the performance of the two-phase framework in both phases and surpass the mainstream end-to-end models.
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