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GWENN-SS: a simple semi-supervised nearest-neighbor density-based classification method with application to hyperspectral images
Author(s): Claude Cariou; Kacem Chehdi; Steven Le Moan
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Paper Abstract

In this communication, we address the problem of semi-supervised classification under conditions where (i) learning samples are available only for specific classes and potentially mislabeled, and (ii) the actual number of classes is unknown. For this, we propose a semi-supervised extension of a Nearest-Neighbor - Density Based clustering method, namely the Graph WatershEd using Nearest Neighbor (GWENN) method. We show how an incomplete, erroneous learning sample (LS) set can be incorporated in the algorithm in order to produce efficient labeling decisions partly guided by a priori information, and to discover new classes and correct mislabeled objects. The efficiency of the proposed method, named GWENN-SS, is demonstrated experimentally. We first evaluate its robustness with simulated data for which an erroneous and incomplete LS set is given. We then assess the reliability of GWENN-SS on real hyperspectral images and we show that it can outperform a recent similar semi-supervised approach.

Paper Details

Date Published: 7 October 2019
PDF: 9 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111550J (7 October 2019); doi: 10.1117/12.2533140
Show Author Affiliations
Claude Cariou, Ecole Nationale Supérieure des Sciences Appliquées et de Technologie (France)
Kacem Chehdi, Ecole Nationale Supérieure des Sciences Appliquées et de Technologie, Univ. Rennes (France)
Steven Le Moan, Massey Univ. (New Zealand)


Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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