常用的人工判读方式需要耗费大量的人力资源,成本高且周期长。针对这些问题,基于视觉Transformer的深度学习网络模型对成都市重点区域开展典型地物要素分类,智能提取耕地、林地、道路、大棚、水体等8类地物的分布情况,并通过规则化算法对建筑提取结果进行优化。实验结果表明该方法的分类精度达到84.25%,较U-Net、HRNet、DeeplabV3+分类精度更高,且对成都市约6 400 km
Han Kai,Wang Yunhe,Chen Hanting,Chen Xinghao,Guo Jianyuan,Liu Zhenhua,Tang Yehui,Xiao An,Xu Chunjing,Xu Yixing,Yang Zhaohui,Zhang Yiman,Tao Dacheng.A Survey on Vision Transformer.[J].IEEE transactions on pattern analysis and machine intelligence,2022.
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