Unsupervised deep domain adaptation for hyperspectral image classification

Wei Li, Wei Wei, Lei Zhang, Cong Wang, Yanning Zhang

科研成果: 会议稿件论文同行评审

13 引用 (Scopus)

摘要

Deep neural networks have been proven to be a promising way for hyperspectral image (HSI) classification. Their success depends on a premise that source domain (i.e., training) and target domain (i.e., test) samples are identically distributed. However, due to various imaging environments, in practice obvious distribution discrepancy often exists between these two domains, which can dramatically reduce the capacity of the classifier trained in source domain generalizing to target domain. To mitigate this problem, we present a novel deep unsupervised domain adaptation framework for HSI classification, which can simultaneously align the distributions of two domains and learn a classifier in source domain. Firstly, we employ two auto-encoder networks to separately project the samples from two domains into two low-dimensional feature spaces. Then, a multi-level maximum mean discrepancy (MMD) loss is imposed on the feature space to reduce the distribution discrepancy between two domains. Given the resultant features, a classification subnet is further learned to classify the labeled samples in source domain. Since the classifier is trained based on the domain-invariant features, it can well generalize to the target domain. Experimental results on one benchmark cross-domain HSI datasets prove the superior performance of the proposed method.

源语言英语
DOI
出版状态已出版 - 2019
活动39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, 日本
期限: 28 7月 20192 8月 2019

会议

会议39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
国家/地区日本
Yokohama
时期28/07/192/08/19

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