TY - CONF
T1 - Unsupervised deep domain adaptation for hyperspectral image classification
AU - Li, Wei
AU - Wei, Wei
AU - Zhang, Lei
AU - Wang, Cong
AU - Zhang, Yanning
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Deep learning
KW - Doman adaptation
KW - Hyperspectral image classification
UR - http://www.scopus.com/inward/record.url?scp=85079342626&partnerID=8YFLogxK
U2 - 10.1109/IGARSS40859.2019.8976372
DO - 10.1109/IGARSS40859.2019.8976372
M3 - 论文
AN - SCOPUS:85079342626
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -