Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 44, Issue 11, Pages 3408-3417Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2006.878442
Keywords
hierarchical classifier; knowledge transfer; multitemporal data; semisupervised classifiers; spatially separate data
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Obtaining ground truth for classification of remotely sensed data is time consuming and expensive, resulting in poorly represented signatures over large areas. In addition, the spectral signatures of a given class vary with location and/or time. Therefore, successful adaptation of a classifier designed from the available labeled data to classify new hyperspectral images acquired over other geographic locations or subsequent times is difficult, if minimal additional labeled data are available. In this paper, the binary hierarchical classifier is used to propose a knowledge transfer framework that leverages the information extracted from the existing labeled data to classify spatially separate and multitemporal test data. Experimental results show that in the absence of any labeled data in the new area, the approach is better than a direct application of the original classifier on the new data. Moreover, when small amounts of the labeled data are available from the new area, the framework offers further improvements through semisupervised learning mechanisms and compares favorably with previously proposed methods.
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