Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images
This paper deals with the problem of the classification of large-scale very high-resolution (VHR) remote sensing (RS) images in a semisupervised scenario, where we have a limited training set (less than ten training samples per class). Typical pixel-based classification methods are unfeasible for la...
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Series: | Journal of Sensors |
Online Access: | http://dx.doi.org/10.1155/2018/6257810 |
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doaj-830eccf043734e669cba8e7fd121921f2020-11-24T22:25:04ZengHindawi LimitedJournal of Sensors1687-725X1687-72682018-01-01201810.1155/2018/62578106257810Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing ImagesHaikel Alhichri0Essam Othman1Mansour Zuair2Nassim Ammour3Yakoub Bazi4Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaComputer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaComputer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaComputer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaComputer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaThis paper deals with the problem of the classification of large-scale very high-resolution (VHR) remote sensing (RS) images in a semisupervised scenario, where we have a limited training set (less than ten training samples per class). Typical pixel-based classification methods are unfeasible for large-scale VHR images. Thus, as a practical and efficient solution, we propose to subdivide the large image into a grid of tiles and then classify the tiles instead of classifying pixels. Our proposed method uses the power of a pretrained convolutional neural network (CNN) to first extract descriptive features from each tile. Next, a neural network classifier (composed of 2 fully connected layers) is trained in a semisupervised fashion and used to classify all remaining tiles in the image. This basically presents a coarse classification of the image, which is sufficient for many RS application. The second contribution deals with the employment of the semisupervised learning to improve the classification accuracy. We present a novel semisupervised approach which exploits both the spectral and spatial relationships embedded in the remaining unlabelled tiles. In particular, we embed a spectral graph Laplacian in the hidden layer of the neural network. In addition, we apply regularization of the output labels using a spatial graph Laplacian and the random Walker algorithm. Experimental results obtained by testing the method on two large-scale images acquired by the IKONOS2 sensor reveal promising capabilities of this method in terms of classification accuracy even with less than ten training samples per class.http://dx.doi.org/10.1155/2018/6257810 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Haikel Alhichri Essam Othman Mansour Zuair Nassim Ammour Yakoub Bazi |
spellingShingle |
Haikel Alhichri Essam Othman Mansour Zuair Nassim Ammour Yakoub Bazi Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images Journal of Sensors |
author_facet |
Haikel Alhichri Essam Othman Mansour Zuair Nassim Ammour Yakoub Bazi |
author_sort |
Haikel Alhichri |
title |
Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images |
title_short |
Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images |
title_full |
Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images |
title_fullStr |
Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images |
title_full_unstemmed |
Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images |
title_sort |
tile-based semisupervised classification of large-scale vhr remote sensing images |
publisher |
Hindawi Limited |
series |
Journal of Sensors |
issn |
1687-725X 1687-7268 |
publishDate |
2018-01-01 |
description |
This paper deals with the problem of the classification of large-scale very high-resolution (VHR) remote sensing (RS) images in a semisupervised scenario, where we have a limited training set (less than ten training samples per class). Typical pixel-based classification methods are unfeasible for large-scale VHR images. Thus, as a practical and efficient solution, we propose to subdivide the large image into a grid of tiles and then classify the tiles instead of classifying pixels. Our proposed method uses the power of a pretrained convolutional neural network (CNN) to first extract descriptive features from each tile. Next, a neural network classifier (composed of 2 fully connected layers) is trained in a semisupervised fashion and used to classify all remaining tiles in the image. This basically presents a coarse classification of the image, which is sufficient for many RS application. The second contribution deals with the employment of the semisupervised learning to improve the classification accuracy. We present a novel semisupervised approach which exploits both the spectral and spatial relationships embedded in the remaining unlabelled tiles. In particular, we embed a spectral graph Laplacian in the hidden layer of the neural network. In addition, we apply regularization of the output labels using a spatial graph Laplacian and the random Walker algorithm. Experimental results obtained by testing the method on two large-scale images acquired by the IKONOS2 sensor reveal promising capabilities of this method in terms of classification accuracy even with less than ten training samples per class. |
url |
http://dx.doi.org/10.1155/2018/6257810 |
work_keys_str_mv |
AT haikelalhichri tilebasedsemisupervisedclassificationoflargescalevhrremotesensingimages AT essamothman tilebasedsemisupervisedclassificationoflargescalevhrremotesensingimages AT mansourzuair tilebasedsemisupervisedclassificationoflargescalevhrremotesensingimages AT nassimammour tilebasedsemisupervisedclassificationoflargescalevhrremotesensingimages AT yakoubbazi tilebasedsemisupervisedclassificationoflargescalevhrremotesensingimages |
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1725759621568659456 |