Multiple Kernel-Based SVM Classification of Hyperspectral Images by Combining Spectral, Spatial, and Semantic Information
In this study, we present a hyperspectral image classification method by combining spectral, spatial, and semantic information. The main steps of the proposed method are summarized as follows: First, principal component analysis transform is conducted on an original image to produce its extended mor...
Main Authors: | Yi Wang, Wenke Yu, Zhice Fang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-01-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/12/1/120 |
Similar Items
-
Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification
by: Ke Wang, et al.
Published: (2020-07-01) -
Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine
by: Chen Chen, et al.
Published: (2014-06-01) -
Land Cover Classification from Hyperspectral Images via Weighted Spatial-Spectral Kernel Collaborative Representation with Tikhonov Regularization
by: Rongchao Yang, et al.
Published: (2022-02-01) -
Multi-Branch Hybrid Network Based on Adaptive Selection of Spatial-Spectral Kernel for Hyperspectral Image Classification
by: Cailing Wang, et al.
Published: (2023-01-01) -
Multiscale Weighted Adjacent Superpixel-Based Composite Kernel for Hyperspectral Image Classification
by: Yaokang Zhang, et al.
Published: (2021-02-01)