Classification of multi-source sensor data with limited labeled data

Melba M. Crawford; Saurabh Prasad; Xiong Zhou; Zhou Zhang

doi:10.1117/12.2180672

10 June 2015 Classification of multi-source sensor data with limited labeled data

Melba M. Crawford, Saurabh Prasad, Xiong Zhou, Zhou Zhang

Proceedings Volume 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI; 94720Y (2015) https://doi.org/10.1117/12.2180672
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

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.

Citation Download Citation

Melba M. Crawford, Saurabh Prasad, Xiong Zhou, and Zhou Zhang "Classification of multi-source sensor data with limited labeled data", Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 94720Y (10 June 2015); https://doi.org/10.1117/12.2180672

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available