elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data

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.

[img] HTML
3kB
[img] PDF
286kB

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/
Document Type:Article
Title:A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ghamisi, PedramDLR-IMF/TUM-LMFUNSPECIFIED
Chen, YushiHarbin Institute of TechnologyUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
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:
EditorsEmailEditor's ORCID iD
Frery, Alejandro C.acfrery@gmail.comUNSPECIFIED
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

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.