Image classification of hyperspectral remote sensing using semi-supervised learning algorithm
Hyperspectral images contain abundant spectral and spatial information of the surface of the earth, but there are more difficulties in processing, analyzing, and sample-labeling these hyperspectral images. In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression...
Main Authors: | Ansheng Ye, Xiangbing Zhou, Kai Weng, Yu Gong, Fang Miao, Huimin Zhao |
---|---|
Format: | Article |
Language: | English |
Published: |
AIMS Press
2023-05-01
|
Series: | Mathematical Biosciences and Engineering |
Subjects: | |
Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023510?viewType=HTML |
Similar Items
-
Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
by: Ling Dai, et al.
Published: (2021-11-01) -
Hyperspectral Remote Sensing Image Classification With CNN Based on Quantum Genetic-Optimized Sparse Representation
by: Huayue Chen, et al.
Published: (2020-01-01) -
Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection
by: Fen Cai, et al.
Published: (2019-09-01) -
Feature Extraction Based Multi-Structure Manifold Embedding for Hyperspectral Remote Sensing Image Classification
by: Yuhang Gan, et al.
Published: (2017-01-01) -
Hyperspectral imagery super-resolution by sparse representation and spectral regularization
by: Zhao Yongqiang, et al.
Published: (2011-01-01)