PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Neural Adaptive Model for feature selection and hyperspectral data classification
Ignazio Gallo, Elisabetta Binaghi and Mirco Boschetti
Image and Signal Processing for Remote Sensing IX Volume 5573, 2004. ISSN 0277-786X

Abstract

Hyperspectral imaging is becoming an important analytical tool for generating land-use map. High dimensionality in hyperspectral remote sensing data, on one hand, provides us with more potential discrimination power for classification tasks. On the other hand, the classification performance improves up to a limited point as additional features are added, and then deteriorates due to the limited number of training samples. Proceeding from these considerations, the present work is aimed to systematically evaluate the robustness of novel classification techniques in classifying hyperspectral data under the twofold condition of high dimensionality and minimal training. We consider in the study a neural adaptive model based on Multi Layer Perceptron (MLP). Accuracy has been evaluated experimentally, classifying MIVIS Hyperspectral data to identify different typology of vegetation in Ticino Regional Park. A performance analysis has been conducted comparing the novel approach with Support Vector Machine and conventional statistical and neural techniques. The adaptive model shows advantages especially when mixed data are presented to the classifiers in combination with minimal training conditions.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:269
Deposited By:Ignazio Gallo
Deposited On:23 November 2004