DW-D³A: dynamic weighted dual-driven domain adaptation for cross-scene hyperspectral image classification

ABSTRACT

Domain adaptation (DA) offers an effective way to align feature distributions of the source domain (SD) and the target domain (TD) without requiring any target label samples. As a method of DA, representation learning effectively realizes the alignment of feature distributions in different domains by transferring domain knowledge. However, existing representation learning methods often focus on unilateral representation transfer, which potentially results in transfer bias. Additionally, most methods ignore the connection between domain alignment and discrimination during the DA process, which easily causes negative transfer. This paper proposes a dynamic weighted dual-driven domain adaptation (DW-D${ }^3$A) model that effectively addresses the aforementioned issues through bilateral feature transfer between domains and a dynamic weighted scheme. Technically, we first propose a dual-driven domain adaptation (D${ }^3$A) model, which employs symmetrical structures to facilitate the knowledge transfer of bilateral representations between source and target domain samples, learning the subspaces of two domains and reducing distribution discrepancies between subspaces via joint distribution-driven alignment. This process mitigates transfer bias and goes beyond previous unilateral transfer methods. Then, to alleviate strong constraints on projecting SD and TD into the same subspace in existing approaches, we apply a relaxed subspace constraint to bring the projections of SD and TD closer. Furthermore, data reconstruction is incorporated to preserve discriminant information from the original data. Lastly, we expand (D${ }^3$A) to DW-D${ }^3$A using a dynamic weighted scheme, which adjusts the weights assigned to domain alignment and discrimination based on their significance to inhibit negative transfer. Extensive experimentation on three datasets indicates that DW-D${ }^3$A outperforms seven other DA methods, showing its superior performance.

KEYWORDS:

• Cross-scene
• domain adaptation
• distribution alignment
• hyperspectral image classification

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in this paper can be downloaded from the following:

• Pavia: https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_Centre_and_University.

• Hyrank: https://zenodo.org/records/1222202.

• Houston: https://hyperspectral.ee.uh.edu/?page_id=459.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China under Grant [2022YFD2000500]; the National Natural Science Foundation of China under Grant [62071157] and the Natural Science Foundation of Heilongjiang Province, China, under Grant YO2019F011.