Adaptive testing method for cross domain semantic segmentation of aerial images

doi:10.13474/j.cnki.11-2246.2026.0116

Abstract

Abstract: To address the issue of limited availability of training data in the target domain for remote sensing images and the inability to perform complete unsupervised domain adaptation during testing,which leads to decreased model accuracy,this paper proposes a test-time adaptation technique for cross-domain semantic segmentation of aerial remote sensing images.Based on a teacher-student network framework,the method introduces spatial consistency constraints and a high-confidence pseudo-labeling mechanism,enabling real-time and continuous model optimization in the target domain without requiring source domain data or annotations.Experimental results demonstrate the superior cross-domain adaptation capability of the proposed method in cross-band scenarios of aerial remote sensing imagery.On the target domain,the model's IoU increased from 27.51% to 42.63%,significantly outperforming comparative methods.Furthermore,the method exhibits strong extensibility and can be integrated with the DRDG (depth-assisted resi-DualGAN) approach to construct a multi-stage adaptive framework,which further improves IoU by over 5%.The proposed method demonstrates excellent cross-domain adaptation capability for aerial remote sensing image semantic segmentation,along with strong extensibility,effectiveness,and robustness in complex cross-domain scenarios.

Key words: aviation semantic segmentation, unsupervised domain adaptation, test-time adaptation, transfer learning

CLC Number:

P237

LUO Huiheng, YANG Lei, WEN Fan, HU Jinyan, WANG Haiyu, LI Zhuojian, GAO Chen. Adaptive testing method for cross domain semantic segmentation of aerial images[J]. Bulletin of Surveying and Mapping, 2026, 0(1): 100-107.

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