· 2015
Deep supervised learning for hyperspectral data classification through convolutional neural networks
- 998citations
- 2015year
Short summary
A deep learning approach using Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs) automates feature extraction for hyperspectral data classification, outperforming traditional methods that rely on handcrafted features.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a deep learning method for hyperspectral data classification.
- Employs a CNN for automated hierarchical feature extraction.
- Uses an MLP for the final classification task.
- Outperforms traditional pattern recognition methods relying on handcrafted features.
- Validated on widely used datasets, showing high accuracy.
AI-generated from the title and abstract; the full text is not read.
Abstract
Spectral observations along the spectrum in many narrow spectral bands through hyperspectral imaging provides valuable information towards material and object recognition, which can be consider as a classification task. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on the construction of complex handcrafted features. However, it is rarely known which features are important for the problem at hand. In contrast to these approaches, we propose a deep learning based classification method that hierarchically constructs high-level features in an automated way. Our method exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task. Experimental results and quantitative validation on widely used datasets showcasing the potential of the developed approach for accurate hyperspectral data classification.
The authors' abstract, as published at the source., 2015 · DOI ↗
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Field: Media Technology
Media TechnologyEngineering