HYPERSPECTRAL IMAGE CLASSIFICATION BY EXPLOITING CONVOLUTIONAL NEURAL NETWORKS

High spectral dimensionality of hyperspectral images makes them useful data resources for earth observation in many remote sensing applications. In this case, the convolutional neural network (CNN) can help to extract deep and robust features from hyperspectral images. The main goal of this paper is...

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Bibliographic Details
Published in:International archives of the photogrammetry, remote sensing and spatial information sciences. remote sensing and spatial information sciences., 2019-10, Vol.XLII-4/W18, p.535-540
Main Authors: Hosseiny, B., Rastiveis, H., Daneshtalab, S.
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Datasets
Earth
Feature extraction
Hyperspectral imaging
Image classification
Machine learning
Multilayers
Neural networks
Remote observing
Remote sensing
Training
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Summary:High spectral dimensionality of hyperspectral images makes them useful data resources for earth observation in many remote sensing applications. In this case, the convolutional neural network (CNN) can help to extract deep and robust features from hyperspectral images. The main goal of this paper is to use deep learning concept to extract deep features from hyperspectral datasets to achieve better classification results. In this study, after pre-processing step, data is fed to a CNN in order to extract deep features. Extracted features are then imported in a multi-layer perceptron (MLP) network as our selected classifier. Obtained classification accuracies, based on training sample size, vary from 94.3 to 97.17% and 92.35 to 98.14% for Salinas and Pavia datasets, respectively. These results expressed more than 10% improvements compared to the classic MLP classification technique.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLII-4-W18-535-2019