IEEE Transactions on Geoscience and Remote Sensing· 2016Q1
Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach
- 1,193citations
- Q1SCImago
- 2016year
Short summary
A novel spectral–spatial feature based classification (SSFC) framework combines balanced local discriminant embedding for spectral feature extraction and convolutional neural networks for spatial feature extraction, outperforming existing methods on hyperspectral datasets.
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Key points
- Proposes a Spectral–Spatial Feature Classification (SSFC) framework for hyperspectral images.
- Utilizes balanced local discriminant embedding for spectral feature extraction.
- Employs convolutional neural networks for automatic spatial feature extraction.
- Fuses spectral and spatial features for enhanced classification.
- Demonstrates superior performance over existing methods on hyperspectral datasets.
AI-generated from the title and abstract; the full text is not read.
Abstract
In this paper, we propose a spectral–spatial feature based classification (SSFC) framework that jointly uses dimension reduction and deep learning techniques for spectral and spatial feature extraction, respectively. In this framework, a balanced local discriminant embedding algorithm is proposed for spectral feature extraction from high-dimensional hyperspectral data sets. In the meantime, convolutional neural network is utilized to automatically find spatial-related features at high levels. Then, the fusion feature is extracted by stacking spectral and spatial features together. Finally, the multiple-feature-based classifier is trained for image classification. Experimental results on well-known hyperspectral data sets show that the proposed SSFC method outperforms other commonly used methods for hyperspectral image classification.
The authors' abstract, as published at the source. IEEE Transactions on Geoscience and Remote Sensing, 2016 · DOI ↗
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Field: Media Technology
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