@inproceedings{13e389f881c247739437e6a22e438f42,
title = "Unsupervised multispectral image classification",
abstract = "This paper presents a new approach to unsupervised classification for multispectral imagery. It first uses a Gaussian pyramid multi-resolution technique to reduce image size from which the pixel purity index (PPI) is implemented to find regions of interest (ROIs) with PPI counts greater than zero. These PPI-found samples are further used as support vectors for a support vector machine (SVM) to classify data. The resulting SVM-classified data samples are further processed by a new designed iterative Fisher's linear discriminate analysis (IFLDA) which implements FLDA in an iterative manner to refine classification results. The experimental results show the proposed approach has great promise in unsupervised classification.",
author = "Chen, {Shih Yu} and Chinsu Lin and Ouyang, {Yen Chieh} and Chang, {Chein I.}",
year = "2012",
doi = "10.1109/WHISPERS.2012.6874289",
language = "English",
isbn = "9781479934065",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012",
address = "United States",
note = "2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012 ; Conference date: 04-06-2012 Through 07-06-2012",
}