Benchmarking Domain Adaptation Methods on Aerial Datasets.

Area of Science:

• Computer Science
• Artificial Intelligence
• Remote Sensing

Background:

• Deep learning excels in supervised classification but assumes identical training and testing data distributions.
• Domain shift, where training and testing data differ, degrades model performance.
• Unsupervised domain adaptation (UDA) addresses this by using labeled source data and unlabeled target data.

Purpose of the Study:

• To overview and benchmark state-of-the-art deep learning UDA models.
• To evaluate UDA performance on novel aerial datasets.
• To provide insights into UDA for remote sensing applications.

Main Methods:

• Overview of seven leading deep learning-based UDA models.
• Creation of three new UDA datasets from public aerial imagery.
• Benchmarking model performance using classification accuracy and t-SNE visualizations.

Main Results:

• Demonstration of UDA model effectiveness in mitigating domain shift for aerial data.
• Comparative analysis of the performance of seven distinct UDA techniques.
• Visualization of feature space adaptation using t-SNE, highlighting improved domain alignment.

Conclusions:

• Unsupervised domain adaptation is vital for robust deep learning in aerial imagery analysis.
• The study provides a foundational benchmark for UDA methods in this domain.
• Findings support the application of UDA to enhance classification accuracy in diverse aerial datasets.