HybridSN: Exploring 3-D-2-D CNN Feature Hierarchy for Hyperspectral Image Classification

HybridSN: Exploring 3-D-2-D CNN Feature Hierarchy for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional neural network (CNN) is one of the most frequently used deep learning-based methods for visual data processing. The use of CNN for...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2020-02, Vol.17 (2), p.277-281
Main Authors: Roy, Swalpa Kumar, Krishna, Gopal, Dubey, Shiv Ram, Chaudhuri, Bidyut B.
Format: Article
Language:eng
Subjects:
2-D-convolutional neural network (CNN)
3-D-CNN
Artificial neural networks
Classification
CNNs
Complexity
Computational modeling
Computer applications
Data analysis
Data processing
Deep learning
Feature extraction
hybrid spectral CNN (HybridSN)
hyperspectral image (HSI) classification
Hyperspectral imaging
Image classification
Imagery
Information processing
IP networks
Kernel
Machine learning
Methods
Neural networks
Principal component analysis
Remote sensing
Representations
Source code
Spectra
Spectral bands
spectral–spatial
Three dimensional models
Visual discrimination learning
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Summary:Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional neural network (CNN) is one of the most frequently used deep learning-based methods for visual data processing. The use of CNN for HSI classification is also visible in recent works. These approaches are mostly based on 2-D CNN. On the other hand, the HSI classification performance is highly dependent on both spatial and spectral information. Very few methods have used the 3-D-CNN because of increased computational complexity. This letter proposes a hybrid spectral CNN (HybridSN) for HSI classification. In general, the HybridSN is a spectral-spatial 3-D-CNN followed by spatial 2-D-CNN. The 3-D-CNN facilitates the joint spatial-spectral feature representation from a stack of spectral bands. The 2-D-CNN on top of the 3-D-CNN further learns more abstract-level spatial representation. Moreover, the use of hybrid CNNs reduces the complexity of the model compared to the use of 3-D-CNN alone. To test the performance of this hybrid approach, very rigorous HSI classification experiments are performed over Indian Pines, University of Pavia, and Salinas Scene remote sensing data sets. The results are compared with the state-of-the-art hand-crafted as well as end-to-end deep learning-based methods. A very satisfactory performance is obtained using the proposed HybridSN for HSI classification. The source code can be found at https://github.com/gokriznastic/HybridSN.
ISSN:1545-598X
1558-0571