Journal of Sensors· 2015Q2
Deep Convolutional Neural Networks for Hyperspectral Image Classification
- 1,887citations
- Q2SCImago
- 2015year
Short summary
A novel deep convolutional neural network (CNN) architecture directly classifies hyperspectral images in the spectral domain, outperforming traditional methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- A five-layer deep CNN architecture is proposed for hyperspectral image classification.
- The CNN operates directly on spectral signatures within the spectral domain.
- Experimental results demonstrate better classification performance than SVM and other deep learning methods.
- The architecture includes input, convolutional, max pooling, full connection, and output layers.
AI-generated from the title and abstract; the full text is not read.
Abstract
Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.
The authors' abstract, as published at the source. Journal of Sensors, 2015 · DOI ↗
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Field: Media Technology
Media TechnologyEngineering