Uncertainty measurement-guided iterative sample selection via shallow convolutional neural network for hyperspectral image classification-Reference-Cited by-同舟云学术

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Uncertainty measurement-guided iterative sample selection via shallow convolutional neural network for hyperspectral image classification

Published:2022-07-29 Issue:03 Volume:16 Page:
ISSN:1931-3195
Container-title:Journal of Applied Remote Sensing
language:
Short-container-title:J. Appl. Rem. Sens.

Author:

Zhang Xin¹,Kang Lei²,Zhang Chunlei³,Zhang Zitong⁴,Jiang Yanan⁵

Affiliation:

1. Beijing Normal University, School of Statistics, Beijing, China

2. Beijing Normal University, School of Artificial Intelligence, Beijing, China

3. Beijing Zhongdi Runde Petroleum Technology Co., Ltd., Beijing, China

4. China University of Geosciences, School of Earth Sciences and Resources, Beijing, China

5. Beijing Normal University, School of Mathematical Sciences, Beijing, China

Publisher

SPIE-Intl Soc Optical Eng

Subject

General Earth and Planetary Sciences

Reference61 articles.

1. Active deep densely connected convolutional network for hyperspectral image classification

2. Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

3. Tailings Reservoir Disaster and Environmental Monitoring Using the UAV-ground Hyperspectral Joint Observation and Processing: A Case of Study in Xinjiang, the Belt and Road

4. Potential of hyperspectral imagery for the spatial assessment of soil erosion stages in agricultural semi-arid Spain at different scales

5. A Novel Semisupervised Active-Learning Algorithm for Hyperspectral Image Classification

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3. LiteCCLKNet: A lightweight criss‐cross large kernel convolutional neural network for hyperspectral image classification;IET Computer Vision;2023-07-24

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