First-Order Cross-Domain Meta Learning for Few-Shot Remote Sensing Object Classification.

Remote sensing images exhibit intrinsic domain complexity arising from multi-source sensor variances, which heterogeneity fundamentally challenges conventional cross-domain few-shot methods that assume simple distribution shifts. Addressing this, we propose a first-order Cross-Domain Meta Learning (CDML) for few-shot remote sensing object classification. CDML implements a dual-stage domain adaptation task as the fundamental meta-learning unit, and includes a cross-domain meta-train phase (CDMTrain) and a cross-domain meta-test phase (CDMTest). In CDMTrain, we propose an inner-loop multi-domain few-shot task sampling, which enables a teacher model encapsulate both cross-category discriminative features and authentic inter-domain distributional divergence. This alternating cyclic learning paradigm captures genuine domain shifts, with each update direction progressively guiding the model toward parameters that balance multi-domain performance. In CDMTest, we evaluate a domain diversity enhancement by transferring teacher parameters to the student model for cross-domain capability assessment on the reserved pseudo-unseen domain. The task-level design progressively improves domain generalization through iterative domain adaptive task learning. Meanwhile, to mitigate the conflicts and inadequacies caused by multi-domain scenarios, we propose a learnable affine transformation model. It adaptively learns affine transformation parameters through intermediate layer features to fine-tune the update direction. Extensive experiments on five remote sensing classification benchmarks demonstrate a superior performance of the proposed method compared with the state-of-the-art methods.