Ghamisi, Pedram and Chen, Yushi and Zhu, Xiao Xiang (2016) A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data. IEEE Geoscience and Remote Sensing Letters, 13 (10), pp. 1537-1541. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2016.2595108. ISSN 1545-598X.
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Official URL: http://ieeexplore.ieee.org/document/7544576/
Abstract
In this letter, a self-improving convolutional neural network (CNN) based method is proposed for the classification of hyperspectral data. This approach solves the so-called curse of dimensionality and the lack of available training samples by iteratively selecting the most informative bands suitable for the designed network via fractional order Darwinian particle swarm optimization. The selected bands are then fed to the classification system to produce the final classification map. Experimental results have been conducted with two well-known hyperspectral data sets: Indian Pines and Pavia University. Results indicate that the proposed approach significantly improves a CNN-based classification method in terms of classification accuracy. In addition, this letter uses the concept of dither for the first time in the remote sensing community to tackle overfitting.
Item URL in elib: | https://elib.dlr.de/106348/ | ||||||||||||
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Document Type: | Article | ||||||||||||
Title: | A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data | ||||||||||||
Authors: |
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Date: | October 2016 | ||||||||||||
Journal or Publication Title: | IEEE Geoscience and Remote Sensing Letters | ||||||||||||
Refereed publication: | Yes | ||||||||||||
Open Access: | Yes | ||||||||||||
Gold Open Access: | No | ||||||||||||
In SCOPUS: | Yes | ||||||||||||
In ISI Web of Science: | Yes | ||||||||||||
Volume: | 13 | ||||||||||||
DOI : | 10.1109/LGRS.2016.2595108 | ||||||||||||
Page Range: | pp. 1537-1541 | ||||||||||||
Editors: |
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Publisher: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||
ISSN: | 1545-598X | ||||||||||||
Status: | Published | ||||||||||||
Keywords: | Convolutional neural network (CNN), deep learning, feature selection, fractional order Darwinian particle swarm optimization (FODPSO), hyperspectral image classification. | ||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||
HGF - Program: | Space | ||||||||||||
HGF - Program Themes: | Earth Observation | ||||||||||||
DLR - Research area: | Raumfahrt | ||||||||||||
DLR - Program: | R EO - Earth Observation | ||||||||||||
DLR - Research theme (Project): | R - Vorhaben hochauflösende Fernerkundungsverfahren (old) | ||||||||||||
Location: | Oberpfaffenhofen | ||||||||||||
Institutes and Institutions: | Remote Sensing Technology Institute > SAR Signal Processing | ||||||||||||
Deposited By: | Ghamisi, Pedram | ||||||||||||
Deposited On: | 19 Oct 2016 09:57 | ||||||||||||
Last Modified: | 31 Jul 2019 20:03 |
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