Geometric-Spectral Reconstruction Learning for Multi-Source Open-Set Classification With Hyperspectral and LiDAR Data

被引：0
|
作者：
Leyuan Fang [1 ,2 ,3 ]
Dingshun Zhu [4 ]
Jun Yue [5 ]
Bob Zhang [1 ,6 ]
Min He [1 ,4 ]
机构：
[1] IEEE
[2] the College of Electrical and Information Engineering,Hunan University
[3] the Peng Cheng Laboratory
[4] the College of Electrical and Information Engineering, Hunan University
[5] the Department of Geomatics Engineering, Changsha University of Science and Technology
[6] the PAMI Research Group, the Department of Computer and Information Science, University of Macau
来源：
IEEE/CAA Journal of Automatica Sinica | 2022年 / 9卷 / 10期
基金：
中国国家自然科学基金;
关键词：
D O I：
暂无
中图分类号：
TP751 [图像处理方法];
学科分类号：
081002 ;
摘要：
Dear editor,This letter presents an open-set classification method of remote sensing images(RSIs) based on geometric-spectral reconstruction learning. More specifically, in order to improve the ability of RSI classification model to adapt to the open-set environment, an openset classification method based on geometric and spectral feature fusion is proposed.
引用
收藏
页码：1892 / 1895
页数：4
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