As a newly developed classification system,the LCZ scheme provides a research framework for Urban Heat Island(UHI)studies and standardizes the worldwide urban temperature observa-tions.With the growing popularity of d...As a newly developed classification system,the LCZ scheme provides a research framework for Urban Heat Island(UHI)studies and standardizes the worldwide urban temperature observa-tions.With the growing popularity of deep learning,deep learning-based approaches have shown great potential in LCZ mapping.Three major cities in China are selected as the study areas.In this study,we design a deep convolutional neural network architecture,named Residual combined Squeeze-and-Excitation and Non-local Network(RSNNet),that consists of the Squeeze-and-Excitation(SE)block and non-local block to classify LCZ using freely available Sentinel-1 SAR and Sentinel-2 multispectral imagery.Overall Accuracy(OA)of 0.9202,0.9524 and 0.9004 for three selected cities are obtained by applying RSNNet and training data of individual city,and OA of 0.9328 is obtained by training RSNNet with data from all three cities.RSNNet outperforms other popular Convolutional Neural Networks(CNNs)in terms of LCZ mapping accuracy.We further design a series of experiments to investigate the effect of different characteristics of Sentinel-1 SAR data on the performance of RSNNet in LCZ mapping.The results suggest that the combination of SAR and multispectral data can improve the accuracy of LCZ classification.The proposed RSNNet achieves an OA of 0.9425 when integrat-ing the three decomposed components with Sentinel-2 multispectral images,2.44%higher than using Sentinel-2 images alone.展开更多
本文以广西北仑河口国家级自然保护区核心区——珍珠湾范围内的红树林为例,通过提取Sentinel-2影像的水体指数、植被指数、红边特征、纹理特征和空间邻域特征,以及先进星载热发射和反射辐射仪全球数字高程模型(Advanced Spaceborne Ther...本文以广西北仑河口国家级自然保护区核心区——珍珠湾范围内的红树林为例,通过提取Sentinel-2影像的水体指数、植被指数、红边特征、纹理特征和空间邻域特征,以及先进星载热发射和反射辐射仪全球数字高程模型(Advanced Spaceborne Thermal Emission and Reflec-tion Radiometer Global Digital Elevation Model,ASTER GDEM)数据的地形特征,分别采用特征相关性分析及特征重要性排序进行多特征组合初选和优选,最后采用面向对象的支持向量机分类方法进行红树林分布信息提取和分析。实验结果表明:Sentinel-2影像原始波段进行红树林分布信息提取生产者精度较高,但用户精度比较低;增加红边特征、纹理特征、空间邻域特征及地形特征后,用户精度均有明显提升;通过对各类型特征做相关性分析,可以在减少数据冗余的同时提升红树林分布信息提取精度,而通过对各类型特征做重要性排序,可以定量分析各类型特征对红树林分布信息提取的贡献,更大程度上减少数据冗余的同时达到更好的红树林提取结果。本文研究成果对使用Sentinel-2影像多特征开展红树林分布信息提取具有参考意义。展开更多
基金the National Natural Science Foundation of China 42090012,41890820,41771452 and 41771454,with[grant numbers 42090012,41890820,41771452,and 41771454]the Key Research and Development Program of Yunnan province in China with[grant number 2018IB023].
文摘As a newly developed classification system,the LCZ scheme provides a research framework for Urban Heat Island(UHI)studies and standardizes the worldwide urban temperature observa-tions.With the growing popularity of deep learning,deep learning-based approaches have shown great potential in LCZ mapping.Three major cities in China are selected as the study areas.In this study,we design a deep convolutional neural network architecture,named Residual combined Squeeze-and-Excitation and Non-local Network(RSNNet),that consists of the Squeeze-and-Excitation(SE)block and non-local block to classify LCZ using freely available Sentinel-1 SAR and Sentinel-2 multispectral imagery.Overall Accuracy(OA)of 0.9202,0.9524 and 0.9004 for three selected cities are obtained by applying RSNNet and training data of individual city,and OA of 0.9328 is obtained by training RSNNet with data from all three cities.RSNNet outperforms other popular Convolutional Neural Networks(CNNs)in terms of LCZ mapping accuracy.We further design a series of experiments to investigate the effect of different characteristics of Sentinel-1 SAR data on the performance of RSNNet in LCZ mapping.The results suggest that the combination of SAR and multispectral data can improve the accuracy of LCZ classification.The proposed RSNNet achieves an OA of 0.9425 when integrat-ing the three decomposed components with Sentinel-2 multispectral images,2.44%higher than using Sentinel-2 images alone.
文摘本文以广西北仑河口国家级自然保护区核心区——珍珠湾范围内的红树林为例,通过提取Sentinel-2影像的水体指数、植被指数、红边特征、纹理特征和空间邻域特征,以及先进星载热发射和反射辐射仪全球数字高程模型(Advanced Spaceborne Thermal Emission and Reflec-tion Radiometer Global Digital Elevation Model,ASTER GDEM)数据的地形特征,分别采用特征相关性分析及特征重要性排序进行多特征组合初选和优选,最后采用面向对象的支持向量机分类方法进行红树林分布信息提取和分析。实验结果表明:Sentinel-2影像原始波段进行红树林分布信息提取生产者精度较高,但用户精度比较低;增加红边特征、纹理特征、空间邻域特征及地形特征后,用户精度均有明显提升;通过对各类型特征做相关性分析,可以在减少数据冗余的同时提升红树林分布信息提取精度,而通过对各类型特征做重要性排序,可以定量分析各类型特征对红树林分布信息提取的贡献,更大程度上减少数据冗余的同时达到更好的红树林提取结果。本文研究成果对使用Sentinel-2影像多特征开展红树林分布信息提取具有参考意义。
