Cited by 151Remote-Sensing Image ClassificationAdvanced Image Fusion TechniquesSpectroscopy and Chemometric Analyses

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Classification of Hyperspectral Images by Gabor Filtering Based Deep Network

Kang, Xudong, Li, Chengchao, Li, Shutao, Lin, Hui·2018·IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI 10.1109/jstars.2017.2767185

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Abstract

In this paper, a novel spectral-spatial classification method based on Gabor filtering and deep network (GFDN) is proposed. First, Gabor features are extracted by performing Gabor filtering on the first three principal components of the hyperspectral image, which can typically characterize the low-level spatial structures of different orientations and scales. Then, the Gabor features and spectral features are simply stacked to form the fused features. Afterwards, deep features are captured by training a stacked sparse autoencoder deep network with the fused features obtained above as inputs. Since the number of training samples of hyperspectral images is often very limited, which negatively affects the classification performance in deep learning, an effective way of constructing virtual samples is designed to generate more training samples, automatically. By jointly utilizing both the real and virtual samples, the parameters of the deep network can be better trained and updated, which can result in classification results of higher accuracies. Experiments performed on four real hyperspectral datasets show that the proposed method outperforms several recently proposed classification methods in terms of classification accuracies.

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Authors: Kang, X., Li, C., Li, S., & Lin, H.
Affiliations: Hunan University (CN)
ORCID: Kang, Xudong
Li, Chengchao
Lin, Hui
Venue: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2018-04
Volume: 11
Issue: 4
Pages: 1166-1178
ISSN: 1939-1404 (ISSN-L)
2151-1535
DOI: 10.1109/jstars.2017.2767185
OpenAlex: W2768309288
Cited by: 151
References: 47
Citations by year: 2026: 2 · 2025: 3 · 2024: 10 · 2023: 15 · 2022: 24 · 2021: 34 · 2020: 27 · 2019: 22 · 2018: 14
FWCI: 14.31
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion)
Funders: National Natural Science Fund of China for International Cooperation and Exchanges (61520106001)
National Natural Science Foundation of China (61601179)
National Natural Science Fund of China for Distinguished Young Scholars (61325007)
Fund of Hunan Province for Science and Technology Plan Project (2017RS3024)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
Keywords: hyperspectral image classification, Gabor filters, Gabor features, spectral-spatial features, feature fusion, stacked sparse autoencoder, deep networks, synthetic training data, limited training data

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Classification of Hyperspectral Images by Gabor Filtering Based Deep Network

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