🛰️ Remote Sensing¶

🤖 AAAI2026 · 8 paper notes

Asymmetric Cross-Modal Knowledge Distillation: Bridging Modalities with Weak Semantic Consistency: This paper proposes a novel paradigm termed Asymmetric Cross-modal Knowledge Distillation (ACKD), realized through the SemBridge framework — comprising two plug-and-play modules, namely self-supervised semantic matching and optimal transport alignment — to enable cross-modal knowledge distillation under weak semantic consistency. This allows multispectral (MS) images collected from different geographic regions to effectively guide RGB-based remote sensing scene classification.
Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments: This paper models conflicting predictions from multiple pre-trained perception models in novel environments as a consistency-based abductive reasoning problem. Error detection rules and domain constraints for each model are encoded as logic programs, and an optimal hypothesis is sought that maximizes prediction coverage while keeping the inconsistency rate below a threshold. The approach achieves an average F1 improvement of 13.6% across 15 aerial test sets.
Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: To address the attenuation of causal treatment effects caused by regression-to-the-mean in ML-based satellite poverty predictions, this paper proposes two post-processing correction methods that require no additional labeled data — Linear Calibration Correction (LCC) and Tweedie local unshrinking — enabling a single prediction map to be reused across multiple downstream causal studies (the "One Map, Many Trials" paradigm). Tweedie correction achieves near-unbiased treatment effect estimation on both simulated and real DHS data.
M3SR: Multi-Scale Multi-Perceptual Mamba for Efficient Spectral Reconstruction: This paper proposes M3SR, a Mamba-based multi-scale multi-perceptual architecture that integrates spatial, frequency, and spectral branches in parallel within a U-Net multi-scale structure. With only 2.17M parameters and 100.9G FLOPs, M3SR surpasses existing state-of-the-art methods on four spectral reconstruction benchmarks.
Machine Learning for Sustainable Rice Production: Region-Scale Monitoring of Water-Saving Practices in Punjab, India: This paper proposes a dimensional classification approach that decouples the recognition of water-saving rice practices into two independent binary classification tasks — a seeding dimension (DSR vs. PTR) and an irrigation dimension (AWD vs. CF). Using only Sentinel-1 SAR imagery, the method achieves seeding F1=0.80 and irrigation F1=0.74, and performs large-scale inference over 3 million+ parcels in Punjab, with district-level adoption rates strongly correlated with government statistics (Spearman ρ=0.69).
Perceive, Act and Correct: Confidence Is Not Enough for Hyperspectral Classification: This paper proposes the CABIN framework, which employs a closed-loop cognitive perceive–act–correct learning mechanism. By replacing naive confidence with epistemic uncertainty to guide sample selection and pseudo-label management in semi-supervised hyperspectral image classification, CABIN significantly outperforms fully supervised baselines while using only 75% of the labeled data.
TDCNet: Spatio-Temporal Context Learning with Temporal Difference Convolution for Moving IRSTD: This paper proposes TDCNet, which unifies temporal difference and 3D convolution into a single Temporal Difference Convolution (TDC). Through re-parameterization, TDC introduces zero additional inference overhead. Combined with TDC-guided spatio-temporal attention (TDCSTA), TDCNet achieves an F1 of 97.12% (AP50 93.83%) on the newly constructed IRSTD-UAV dataset, which contains 15,106 frames of real infrared UAV imagery.
UniABG: Unified Adversarial View Bridging and Graph Correspondence for Unsupervised Cross-View Geo-Localization: This paper proposes UniABG, a two-stage unsupervised cross-view geo-localization framework that employs View-Aware Adversarial Bridging (VAAB) to eliminate the domain gap between UAV and satellite views, followed by Heterogeneous Graph Filtering Calibration (HGFC) to purify cross-view correspondences. UniABG achieves 93.29% Satellite→Drone AP on University-1652, surpassing most supervised methods.