On the effect of synthetic morphological feature vectors on hyperspectral image classification performance

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2015

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Institute of Electrical and Electronics Engineers Inc.

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Abstract

This paper studies the effect of synthetic feature vectors on the classification performance of hyperspectral remote sensing images. As feature vectors, it has been chosen to employ morphological attribute profiles, that have proven themselves in this field. At this early stage of our work, the relatively simple Bootstrapping algorithm has been used for synthetic feature vector generation. Based on experiments conducted on multiple hyperspectral datasets, it has been observed that synthetic feature vectors contribute considerably to classification performance in the case of limited training dataset sizes. © 2015 IEEE.

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Bootstrap, Classification, Extended morphological attribute profile, Hyperspectral image, Remote sensing, Resampling

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3

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2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings -- 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- Malatya -- 113052

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653

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656