Luojia-HSSR:A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet  

作　　者：Yue Xu Jianya Gong Xin Huang Xiangyun Hu Jiayi Li Qiang Li Min Peng 

机构地区：[1]School of Remote Sensing and Information Engineering,Wuhan University,Wuhan,China [2]State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan,China [3]Hubei Luojia Laboratory,Wuhan,China [4]Institute of Artificial Intelligence in Geomatics,Wuhan University,Wuhan,China [5]Data Center,Shenyang Surveying and Mapping Institute Co.,Ltd,Shenyang,China

出　　处：《Geo-Spatial Information Science》2023年第3期289-301,共13页地球空间信息科学学报（英文）

基　　金：supported by the Major Program of the National Natural Science Foundation of China[grant number 92038301];The research was also supported by the National Natural Science Foundation of China[grant number 41971295];the Foundation for Innovative Research Groups of the Natural Science Foundation of Hubei Province[grant number 2020CFA003];the Special Fund of Hubei Luojia Laboratory.

摘　　要：High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained models.In this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China.To our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground coverage.It is an ideal experimental data for spatial-spectral feature extraction.Furthermore,a new deep learning model 3D-HRNet for interpreting HSSR images is proposed.The conv-neck in HRNet is modified to better mine the spatial information of the images.Then,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously.Subsequently,the 3D convolution is inserted into the HRNet to optimize the performance.The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.

关 键 词：High Spatial and Spectral Resolution(HSSR) remotesensing image classification deep learning Convolutional Neural Network(CNN) 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]