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An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical SegmentationAugmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.
Document ID
20170003162
Document Type
Reprint (Version printed in journal)
Authors
Zhang, Zhou (Purdue Univ. West Lafayette, IN, United States)
Pasolli, Edoardo (Purdue Univ. West Lafayette, IN, United States)
Crawford, Melba M. (Purdue Univ. West Lafayette, IN, United States)
Tilton, James C. (NASA Goddard Space Flight Center Greenbelt, MD United States)
Date Acquired
April 7, 2017
Publication Date
December 1, 2015
Publication Information
Publication: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume: 9
Issue: 2
ISSN: 1939-1404
Subject Category
Mathematical and Computer Sciences (General)
Report/Patent Number
GSFC-E-DAA-TN40404
Funding Number(s)
CONTRACT_GRANT: NASA AIST 11-0077
Distribution Limits
Public
Copyright
Other
Keywords
classification
hierarchical segmentation
active learning