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GWENN-SS: a simple semi-supervised nearest-neighbor density-based classification method with application to hyperspectral images

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.