Papers › Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation

Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation

9 Dec 2024arXiv:2412.06664archive 2025-07-28

Shun Zhang, Xuechao Zou, Kai Li, Congyan Lang, Shiying Wang, Pin Tao, Tengfei Cao

Fine-grained remote sensing image segmentation is essential for accurately identifying detailed objects in remote sensing images. Recently, vision transformer models (VTMs) pre-trained on large-scale datasets have demonstrated strong zero-shot generalization. However, directly applying them to specific tasks may lead to domain shift. We introduce a novel end-to-end learning paradigm combining knowledge guidance with domain refinement to enhance performance. We present two key components: the Feature Alignment Module (FAM) and the Feature Modulation Module (FMM). FAM aligns features from a CNN-based backbone with those from the pretrained VTM's encoder using channel transformation and spatial interpolation, and transfers knowledge via KL divergence and L2 normalization constraint. FMM further adapts the knowledge to the specific domain to address domain shift. We also introduce a fine-grained grass segmentation dataset and demonstrate, through experiments on two datasets, that our method achieves a significant improvement of 2.57 mIoU on the grass dataset and 3.73 mIoU on the cloud dataset. The results highlight the potential of combining knowledge transfer and domain adaptation to overcome domain-related challenges and data limitations. The project page is available at https://xavierjiezou.github.io/KTDA/.

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Code

XavierJiezou/KTDA mentioned on GitHub report

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Tasks

Domain Adaptation Image Segmentation Semantic Segmentation Spatial Interpolation Transfer Learning Zero-shot Generalization

Datasets

Introduced by this paper, per the archive.

Fine-Grained Grass Segmentation Dataset

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Semantic Segmentation	Fine-Grained Cloud Segmentation Dataset	KTDA	mIoU	51.49	#3 of 4	Archive leaderboard	report
Semantic Segmentation	Fine-Grained Grass Segmentation Dataset	KTDA	mIoU	50.86	#3 of 10	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Attention Dense Connections Layer Normalization Linear Layer Multi-Head Attention Residual Connection Softmax Vision Transformer

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