Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 121, Issue -, Pages 60-76Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.isprsjprs.2016.08.011
Keywords
Hyperspectral classification; Co-training; Tracking-Learning-Detection
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Funding
- project 'Representation of dynamic 3D scenes using the Atomic Shapes Network model' - Polish National Science Centre [DEC-2011/03/D/ST6/03753]
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We present a novel semi-supervised algorithm for classification of hyperspectral data from remote sensors. Our method is inspired by the Tracking-Learning-Detection (TLD) framework, originally applied for tracking objects in a video stream. TLD introduced the co-training approach called P-N learning, making use of two independent 'experts' (or learners) that scored samples in different feature spaces. In a similar fashion, we formulated the hyperspectral classification task as a co-training problem, that can be solved with the P-N learning scheme. Our method uses both spatial and spectral features of data, extending a small set of initial labelled samples during the process of region growing. We show that this approach is stable and achieves very good accuracy even for small training sets. We analyse the algorithm's performance on several publicly available hyperspectral data sets. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
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