Journal
PATTERN RECOGNITION
Volume 77, Issue -, Pages 306-315Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2017.10.007
Keywords
Active learning; Multi-kernel; Domain adaptation; Hyperspectral image classification; Remote sensing
Funding
- National Natural Science Foundation of China [61402026, 61572388]
- Key R&D Program - The Key Industry Innovation Chain of Shaanxi [2017ZDCXL-GY-05-04-02, 2017ZDCXL-GY-05-02]
- Beijing Municipal Science and Technology Commission [Z171100000117022]
- Foundation of State Key Lab of Software Development Environment [SKLSDE-2016ZX-04]
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Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information using the supervised learning or can hardly exploit the discriminative information borrowed from related domains. To address this issues, in this paper we show a novel framework addressing HSI classification based on the domain adaptation (DA) with active learning (AL). The main idea of our method is to retrain the multi-kernel classifier by utilizing the available labeled samples from source domain, and adding minimum number of the most informative samples with active queries in the target domain. The proposed method adaptively combines multiple kernels, forming a DA classifier that minimizes the bias between the source and target domains. Further equipped with the nested actively updating process, it sequentially expands the training set and gradually converges to a satisfying level of classification performance. We study this active adaptation framework with the Margin Sampling (MS) strategy in the HSI classification task. Our experimental results on two popular HSI datasets demonstrate its effectiveness. (C) 2017 Elsevier Ltd. All rights reserved.
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