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：

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

页码：1892 / 1895

页数：4

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