Classification of multi-source data has recently gained significant attention, as accuracies can often be improved by incorporating complementary information extracted in single and multi-sensor scenarios. Supervised approaches to classification of multi-source remote sensing data are dependent on the availability of representative labeled data, which are often limited relative to the dimensionality of the data for training. To address this problem, in this paper, we propose a new framework in which active learning (AL) and semi-supervised learning (SSL) strategies are combined for multi-source classification of hyperspectral images. First, the spatial-spectral features are represented via the redundant discrete wavelet transform (RDWT). Then, the spatial context provided by the hierarchical segmentation algorithm (HSEG) in conjunction with an unsupervised pruning strategy is exploited to combine AL and SSL. Finally, SVM classification is performed due to the high dimensionality of the feature space. The proposed framework is validated with two benchmark hyperspectral data sets. Higher classification accuracies are obtained by the proposed framework with respect to other state-of-the-art active learning classification approaches.

Date Published: 10 June 2015
PDF: 8 pages
Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 94720Y (10 June 2015); doi: 10.1117/12.2180672

Published in SPIE Proceedings Vol. 9472:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI
Miguel Velez-Reyes; Fred A. Kruse, Editor(s)