计算机科学 ›› 2024, Vol. 51 ›› Issue (11A): 231200064-8.doi: 10.11896/jsjkx.231200064
穆正阳1,2, 戴建国1,2, 张国顺1,2, 侯文庆3, 陈沛沛1,2, 曹宇娟1,2, 许淼淼1,2
MU Zhengyang1,2, DAI Jianguo1,2, ZHANG Guoshun1,2, HOU Wenqing3, CHEN Peipei1,2, CAO Yujuan1,2, XU Miaomiao1,2
摘要: 不透水面作为城市化的重要特征,可直观反映城市化范围,利用遥感影像与计算机视觉检测不同时序间城市边缘区不透水面的变化,是观测城市扩张的有效手段,对于城建规划和城市可持续发展具有重要意义。然而,城市边缘区作为城市和自然环境的过渡区域,地物类型复杂凌乱,具有高度异质性,为变化检测任务带来了巨大挑战。为解决这些问题,该方法采用SE(Squeeze and Excitation)压缩激励结构与多尺度融合模块(Multi-scale Fusion Module,MSFM)对Deeplabv3+网络进行改进优化,构建高精度不透水面变化提取网络MSDANet(Muti-scale Dual-attention Network,MSDANet),实现不透水面变化自动提取。同时,基于Google Earth卫星影像平台,获取2017年与2021年乌鲁木齐城市边缘区可见光影像,构建一个注释良好的高分辨率不透水面变化检测数据集HISCD,并将其开源。通过与7种主流变化检测网络进行对比,MSDANet取得了最好的结果,具有良好的变化提取能力,能够精准提取多种不透水面变化类型。在HISCD测试集中OA,Precision,Recall,F1与MIoU各指标分别达到了90.77%,80.51%,78.83%,79.63%与68.80%。该方法为城市扩张分析提供了一种新颖的方法,为城市空间规划提供了有效的技术支持。
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