Multispectral Landsat image classification using a data clustering algorithm

Wang, Yan; Jamshidi, Mo; Neville, Paul; Bales, Chandra; Morain, S. A.

doi:10.1109/icmlc.2004.1384607

Cited by 2 publications

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An Application of Neural and Probabilistic Unsupervised Methods to Environmental Factor Analysis of Multi-spectral Images

Pugliese

Scarpetta

Esposito

et al. 2005

Image Analysis and Processing – ICIAP 2005

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In this paper we test the performance of two unsupervised clustering strategies for the analysis of LANDSAT multispectral images of the Temples of Paestum Area in Italy. The classification goal is to identify environmental factors (soils, vegetation types, water) on the images, exploiting the features of the seven LANDSAT spectral bands. The first strategy is a fast migrating means technique based on a Maximum Likelihood Principle (ISOCLUST algorithm), and the second is the Kohonen Self Organizing Map (SOM) neural network. The advantage of using the SOM algorithm is that both the information on classes and the similarity between the classes are obtained (since proximity corresponds to similarity among neurons). By exploiting the information on class similarity it was possible to automatically colour each cluster identified by the net (assigning a specific colour to each of them) thus facilitating a successive photo-interpretation.

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An Application of Neural and Probabilistic Unsupervised Methods to Environmental Factor Analysis of Multi-spectral Images

Pugliese

Scarpetta

Esposito

et al. 2005

Image Analysis and Processing – ICIAP 2005

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A Comparative Study of Data Mining Algorithms for Image Classification

Thamilselvana¹,

Sathiaseelan²

2015

IJEME

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Data mining is an important research area in computer science. It is a computational process of determining patterns in large data. Image mining is one of important techniques in data mining, which involved in multiple disciplines. Image Classification Refers the tagging the images into a number of predefined sets. It's also includes image preprocessing, feature extraction, object detection, object classification, object segmentation, object classification and many more techniques.

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Multispectral Landsat image classification using a data clustering algorithm

Cited by 2 publications

References 4 publications

An Application of Neural and Probabilistic Unsupervised Methods to Environmental Factor Analysis of Multi-spectral Images

An Application of Neural and Probabilistic Unsupervised Methods to Environmental Factor Analysis of Multi-spectral Images

A Comparative Study of Data Mining Algorithms for Image Classification

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