arXiv:2610.27883v1 Announce Type: new Abstract: Multispectral aerial object detection aims to exploit spectral-spatial cues to address object detection challenges in complex aerial scenes. Existing m...
Multispectral aerial object detection aims to exploit spectral-spatial cues to address object detection challenges in complex aerial scenes. Existing methods mainly focus on spectral-spatial decouplin...
arXiv:2607.05176v3 Announce Type: replace Abstract: Small object detection (SOD) remains a challenging task in real-world applications. Despite recent advances, existing detectors remain limited by r...
LiG-DETR introduces a Global-Local Reassembly framework for aerial object detection that captures high‑fidelity local features before compression and integrates them into a unified end‑to‑end DETR decoder. The method uses a shared encoder to extract both global and locally magnified features, reassembles the local features according to their spatial positions, and employs Context‑Preserved Selective Reassembly and Density‑Aware Adaptive Query Allocation to reduce redundant computation. Experiments demonstrate significant improvements on small and medium objects while maintaining strong performance on large objects, with favorable accuracy–efficiency trade‑offs and better cross‑domain generalization.
arXiv:2606. 23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details.
RoES is a Rotational Equivariant Selective-frequency fusion network that dynamically separates low- and high-frequency components of infrared-visible images. It uses a trainable rotation-enhanced updater to decouple frequencies, then fuses them with a dual-branch module: a rotation-equivariant Mamba for low-frequency structural dependencies and a polar spectral attention Dual-Fourier block for high-frequency detail refinement. Experiments show RoES outperforms existing methods in fusion quality and downstream object detection, offering a robust multimodal fusion solution.
FoCal is a new frequency‑oriented framework for aerial RGB–IR object detection that explicitly models cross‑modal interaction across different frequency components. It introduces a Frequency‑Aware Dual‑Domain Calibration module to consolidate low‑frequency structural cues while preserving high‑frequency modality‑specific details, and a Discrepancy‑Guided Spectral Modulation module that adaptively enhances, preserves, or attenuates the joint spectrum based on confidence‑weighted amplitude discrepancies. Experiments on DroneVehicle, ESCVehicle, and ATR‑UMOD show FoCal achieving high mAP scores (83.5%, 54.8%, 64.6%) with only 3.0 M parameters and 113.6 FPS, demonstrating a strong accuracy–efficiency trade‑off.
arXiv:2607. 16338v1 Announce Type: cross Abstract: This article presents DMFNet, a dual-backbone multiscale feature fusion framework with residual feature propagation and spatial attention for remote sensing scene classification.
arXiv:2506. 12697v3 Announce Type: replace-cross Abstract: Small-object detection in Unmanned Aerial Vehicle (UAV) imagery requires preserving weak local evidence while using broader context to separate tiny foreground targets from cluttered backgrounds.
MambaMPD is a new segmentation framework that leverages Vision Mamba models for marine pollution detection in remote‑sensing imagery. It introduces two structural priors—Frequency‑Aware Augmentation (FAA) and multi‑scale Edge‑Guided Attention (EGA)—to better capture low‑contrast, fragmented pollution patterns and sharpen boundaries. Experiments on the MADOS and M4D datasets show that MambaMPD outperforms existing methods in mIoU while using far less computation than foundation‑model approaches.
arXiv:2608.20944v1 Announce Type: new Abstract: Multimodal object detection in remote sensing faces challenges due to semantic heterogeneity and modality-specific noise interference. To this end, we...
The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy.