The components of urban surface cover are diversified,and component temperature has greater physical significance and application values in the studies on urban thermal environment.Although the multi-angle retrieval a...The components of urban surface cover are diversified,and component temperature has greater physical significance and application values in the studies on urban thermal environment.Although the multi-angle retrieval algorithm of component temperature has been matured gradually,its application in the studies on urban thermal environment is restricted due to the difficulty in acquiring urban-scale multi-angle thermal infrared data.Therefore,based on the existing multi-source multi-band remote sensing data,access to appropriate urban-scale component temperature is an urgent issue to be solved in current studies on urban thermal infrared remote sensing.Then,a retrieval algorithm of urban component temperature by multi-source multi-band remote sensing data on the basis of MODIS and Landsat TM images was proposed with expectations achieved in this work,which was finally validated by the experiment on urban images of Changsha,China.The results show that：1） Mean temperatures of impervious surface components and vegetation components are the maximum and minimum,respectively,which are in accordance with the distribution laws of actual surface temperature; 2） High-accuracy retrieval results are obtained in vegetation component temperature.Moreover,through a contrast between retrieval results and measured data,it is found that the retrieval temperature of impervious surface component has the maximum deviation from measured temperature and its deviation is greater than 1 ℃,while the deviation in vegetation component temperature is relatively low at 0.5 ℃.展开更多
针对遥感建筑物图像中建筑物大小不一、边缘模糊导致精度不高的问题,提出一种双分支并行融合注意力机制的网络模型TC-UNet++。针对卷积神经网络擅长提取局部特征,难以捕获全局信息的特点,引入Transformer结构以解决全局信息丢失的问题...针对遥感建筑物图像中建筑物大小不一、边缘模糊导致精度不高的问题,提出一种双分支并行融合注意力机制的网络模型TC-UNet++。针对卷积神经网络擅长提取局部特征,难以捕获全局信息的特点,引入Transformer结构以解决全局信息丢失的问题。对于两种结构的特征维度和通道数不匹配的问题,设计一种TC(Transformer to CNN)模块以交互的方式融合不同分辨率下局部与全局特征。引入坐标注意力机制,根据像素在图像中的位置信息,定位和识别建筑物。实验结果表明,TC-UNet++在WHU数据集上交互比、准确率、总精度分别达到了93.1%、95.9%、98.8%,在不显著增加参数的情况下,展现出良好的有效性。展开更多
基金Projects(41171326,40771198)supported by the National Natural Science Foundation of ChinaProject(08JJ6023)supported by the Natural Science Foundation of Hunan Province,China
文摘The components of urban surface cover are diversified,and component temperature has greater physical significance and application values in the studies on urban thermal environment.Although the multi-angle retrieval algorithm of component temperature has been matured gradually,its application in the studies on urban thermal environment is restricted due to the difficulty in acquiring urban-scale multi-angle thermal infrared data.Therefore,based on the existing multi-source multi-band remote sensing data,access to appropriate urban-scale component temperature is an urgent issue to be solved in current studies on urban thermal infrared remote sensing.Then,a retrieval algorithm of urban component temperature by multi-source multi-band remote sensing data on the basis of MODIS and Landsat TM images was proposed with expectations achieved in this work,which was finally validated by the experiment on urban images of Changsha,China.The results show that：1） Mean temperatures of impervious surface components and vegetation components are the maximum and minimum,respectively,which are in accordance with the distribution laws of actual surface temperature; 2） High-accuracy retrieval results are obtained in vegetation component temperature.Moreover,through a contrast between retrieval results and measured data,it is found that the retrieval temperature of impervious surface component has the maximum deviation from measured temperature and its deviation is greater than 1 ℃,while the deviation in vegetation component temperature is relatively low at 0.5 ℃.
文摘针对遥感建筑物图像中建筑物大小不一、边缘模糊导致精度不高的问题,提出一种双分支并行融合注意力机制的网络模型TC-UNet++。针对卷积神经网络擅长提取局部特征,难以捕获全局信息的特点,引入Transformer结构以解决全局信息丢失的问题。对于两种结构的特征维度和通道数不匹配的问题,设计一种TC(Transformer to CNN)模块以交互的方式融合不同分辨率下局部与全局特征。引入坐标注意力机制,根据像素在图像中的位置信息,定位和识别建筑物。实验结果表明,TC-UNet++在WHU数据集上交互比、准确率、总精度分别达到了93.1%、95.9%、98.8%,在不显著增加参数的情况下,展现出良好的有效性。
