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

页码：1892 / 1895

页数：4

共 50 条

[31] Combining machine learning and ontological data handling for multi-source classification of nature conservation areas
Moran, Nikias
Nieland, Simon
Suntrup, Gregor Tintrup gen.
Kleinschmit, Birgit
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2017, 54 : 124 - 133
[32] Classification of Agricultural Plastic Cover Based on Multi-kernel Active Learning and Multi-source Data Fusion
Feng Q.
Niu B.
Zhu D.
Liu Y.
Ou C.
Liu J.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2022, 53 (02): : 177 - 185
[33] Multi-level interactive fusion network based on adversarial learning for fusion classification of hyperspectral and LiDAR data
Fan, Yingying
Qian, Yurong
Gong, Weijun
Chu, Zhuang
Qin, Yugang
Muhetaer, Palidan
EXPERT SYSTEMS WITH APPLICATIONS, 2024, 257
[34] EVALUATION OF ESA CCI ABOVE GROUND BIOMASS USING MULTI-SOURCE, MULTI-SPECTRAL DATA AND MACHINE LEARNING
Srivastava, Harsh
Pant, Triloki
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 3082 - 3085
[35] Toulouse Hyperspectral Data Set: A benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques
Thoreau, Romain
Risser, Laurent
Achard, Veronique
Berthelot, Beatrice
Briottet, Xavier
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2024, 212 : 323 - 337
[36] MACHINE LEARNING-BASED ECONOMIC DEVELOPMENT MAPPING FROM MULTI-SOURCE OPEN GEOSPATIAL DATA
Cao, Rui
Tu, Wei
Cai, Jixuan
Zhao, Tianhong
Xiao, Jie
Cao, Jinzhou
Gao, Qili
Su, Hanjing
XXIV ISPRS CONGRESS IMAGING TODAY, FORESEEING TOMORROW, COMMISSION IV, 2022, 5-4 : 259 - 266
[37] Machine Learning Fusion Multi-Source Data Features for Classification Prediction of Lunar Surface Geological Units
Zuo, Wei
Zeng, Xingguo
Gao, Xingye
Zhang, Zhoubin
Liu, Dawei
Li, Chunlai
REMOTE SENSING, 2022, 14 (20)
[38] An approach for accurate identification and monitoring of species in mangrove forests based on multi-source spectral data and deep learning
Erandi, Monterrubio-Martinez
Rubicel, Trujillo-Acatitla
Jose, Tuxpan-Vargas
Patricia, Moreno-Casasola
ECOLOGICAL INFORMATICS, 2025, 85
[39] Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning
Tu, Ying
Chen, Bin
Lang, Wei
Chen, Tingting
Li, Miao
Zhang, Tao
Xu, Bing
REMOTE SENSING, 2021, 13 (21)
[40] Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification
Cui, Yanwei
Chapel, Laetitia
Lefevre, Sebastien
REMOTE SENSING, 2017, 9 (03)

← 1 2 3 4 5 →