A semi-supervised classification algorithm using the TAD-derived background as training data

Lei Fan; Brittany Ambeau; David W. Messinger

doi:10.1117/12.2015810

18 May 2013 A semi-supervised classification algorithm using the TAD-derived background as training data

Lei Fan, Brittany Ambeau, David W. Messinger

Proceedings Volume 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX; 87431Y (2013) https://doi.org/10.1117/12.2015810
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

In general, spectral image classification algorithms fall into one of two categories: supervised and unsupervised. In unsupervised approaches, the algorithm automatically identifies clusters in the data without a priori information about those clusters (except perhaps the expected number of them). Supervised approaches require an analyst to identify training data to learn the characteristics of the clusters such that they can then classify all other pixels into one of the pre-defined groups. The classification algorithm presented here is a semi-supervised approach based on the Topological Anomaly Detection (TAD) algorithm. The TAD algorithm defines background components based on a mutual k-Nearest Neighbor graph model of the data, along with a spectral connected components analysis. Here, the largest components produced by TAD are used as regions of interest (ROI's),or training data for a supervised classification scheme. By combining those ROI's with a Gaussian Maximum Likelihood (GML) or a Minimum Distance to the Mean (MDM) algorithm, we are able to achieve a semi supervised classification method. We test this classification algorithm against data collected by the HyMAP sensor over the Cooke City, MT area and University of Pavia scene.

Citation Download Citation

Lei Fan, Brittany Ambeau, and David W. Messinger "A semi-supervised classification algorithm using the TAD-derived background as training data", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87431Y (18 May 2013); https://doi.org/10.1117/12.2015810

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