@inproceedings{6fd8af11777049deb45f0a7f9d195194,
title = "Multi-temporal satellite image classification using spectral class database",
abstract = "Many computer-assisted classification algorithms have been developed to produce the land-cover maps from multi-spectral satellite images. Since the influence of the atmospheric conditions and the change of the earth surface, most of the image classification methods are image-dependent. For instance, a great number of supervised classification algorithms normally can not apply the same training data to multi-temporal image classification. As a result, the training data has to be re-selected image by image in order to perform multi-temporal image classification. The purpose of this study is to develop an image-invariant classification technique to classify multi-temporal satellite images.",
keywords = "Multi-temporal image classification, Principal components analysis, Pseudo invariant features",
author = "Ju, {Ching Luen} and Chen, {Chi Farn} and Chang, {Li Yu} and Chang, {Hung Yu}",
year = "2006",
language = "???core.languages.en_GB???",
isbn = "9781604231380",
series = "Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006",
pages = "979--984",
booktitle = "Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006",
note = "27th Asian Conference on Remote Sensing, ACRS 2006 ; Conference date: 09-10-2006 Through 13-10-2006",
}