Neighborhood Geometry–Guided Prototype Contrastive Adaptation for Cross-Domain Remote Sensing Scene Classification

Abstract

Cross-domain remote sensing scene classification suffers from severe domain shifts caused by sensor and geographic variations. Existing adversarial domain adaptation methods mainly align global feature distributions but often neglect category-level structure, leading to ambiguous decision boundaries in the target domain. In this paper, we propose a neighborhood geometry–guided prototype contrastive adaptation (NGPCA) framework built upon the domain-adversarial neural network. The proposed method leverages local neighborhood geometry in the target feature space to construct neighborhood-consensus pseudo labels and further introduces prototype contrastive learning to align target features with source-domain class prototypes. By combining neighborhood consensus supervision with prototype-level contrastive adaptation, NGPCA improves pseudo-label robustness and promotes more discriminative feature learning. Extensive experiments on University of California, Merced land-use data set, the Aerial Image Data Set, and the NWPU-RESISC45 data set with six cross-domain transfer tasks demonstrate the effectiveness of the proposed framework. NGPCA achieves the best average accuracy of 98.26% among the compared remote sensing–oriented and general domain adaptation methods, showing robust performance for cross-domain remote sensing scene classification