The University of Southampton
University of Southampton Institutional Repository

The use of small training sets containing mixed pixels for accurate hard image classification: training on mixed spectral responses for classification by a SVM

The use of small training sets containing mixed pixels for accurate hard image classification: training on mixed spectral responses for classification by a SVM
The use of small training sets containing mixed pixels for accurate hard image classification: training on mixed spectral responses for classification by a SVM
The accuracy of a supervised image classification is a function of the training data used in its generation. It is, therefore, critical that the training stage of a supervised classification is designed to provide the necessary information. Guidance on the design of the training stage of a classification typically calls for the use of a large sample of randomly selected pure pixels in order to characterise the classes. Such guidance is generally made without regard to the specific nature of the application in-hand, including the classifier to be used. The design of the training stage should really be based on the classifier to be used since individual training cases can vary in value as can any one training set to a range of classifiers. It is argued here that the training stage can be designed on the basis of the way the classifier operates and with emphasis on the desire to separate the classes rather than describe them. An approach to the training of a support vector machine (SVM) classifier that is the opposite of that generally promoted for training set design is suggested. This approach uses a small sample of mixed spectral responses drawn from purposefully selected locations (geographical boundaries) in training. The approach is based on mixed pixels which are normally masked-out of analyses as undesirable and problematic. A sample of such data should, however, be easier and cheaper to acquire than that suggested by conventional approaches. This new approach to training set design was evaluated against conventional approaches with a set of classifications of agricultural crops from satellite sensor data. The main result was that classifications derived from the use of the mixed spectral responses and the conventional approach did not differ significantly, with the overall accuracy of classifications generally 92%.
Keywords: Training set; Mixed pixel; Support vector machine; Classification
training set, mixed pixel, support vector machine, classification
0034-4257
179-189
Foody, Giles M.
62843823-1717-4a6e-9dd6-72539e7bf44e
Mathur, Ajay
ac6e806b-cb9f-4f50-a714-2bd12d5783af
Foody, Giles M.
62843823-1717-4a6e-9dd6-72539e7bf44e
Mathur, Ajay
ac6e806b-cb9f-4f50-a714-2bd12d5783af

Foody, Giles M. and Mathur, Ajay (2006) The use of small training sets containing mixed pixels for accurate hard image classification: training on mixed spectral responses for classification by a SVM. Remote Sensing of Environment, 103 (2), 179-189. (doi:10.1016/j.rse.2006.04.001).

Record type: Article

Abstract

The accuracy of a supervised image classification is a function of the training data used in its generation. It is, therefore, critical that the training stage of a supervised classification is designed to provide the necessary information. Guidance on the design of the training stage of a classification typically calls for the use of a large sample of randomly selected pure pixels in order to characterise the classes. Such guidance is generally made without regard to the specific nature of the application in-hand, including the classifier to be used. The design of the training stage should really be based on the classifier to be used since individual training cases can vary in value as can any one training set to a range of classifiers. It is argued here that the training stage can be designed on the basis of the way the classifier operates and with emphasis on the desire to separate the classes rather than describe them. An approach to the training of a support vector machine (SVM) classifier that is the opposite of that generally promoted for training set design is suggested. This approach uses a small sample of mixed spectral responses drawn from purposefully selected locations (geographical boundaries) in training. The approach is based on mixed pixels which are normally masked-out of analyses as undesirable and problematic. A sample of such data should, however, be easier and cheaper to acquire than that suggested by conventional approaches. This new approach to training set design was evaluated against conventional approaches with a set of classifications of agricultural crops from satellite sensor data. The main result was that classifications derived from the use of the mixed spectral responses and the conventional approach did not differ significantly, with the overall accuracy of classifications generally 92%.
Keywords: Training set; Mixed pixel; Support vector machine; Classification

This record has no associated files available for download.

More information

Published date: 30 July 2006
Keywords: training set, mixed pixel, support vector machine, classification

Identifiers

Local EPrints ID: 57703
URI: http://eprints.soton.ac.uk/id/eprint/57703
ISSN: 0034-4257
PURE UUID: 8801b37a-abaf-4cda-916b-5dc44ccb486c

Catalogue record

Date deposited: 11 Aug 2008
Last modified: 15 Mar 2024 11:08

Export record

Altmetrics

Contributors

Author: Giles M. Foody
Author: Ajay Mathur

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×