The field of remote sensing is witnessing significant advancements with the integration of multimodal data, including optical, hyperspectral, and synthetic aperture radar (SAR) imagery. Researchers are exploring innovative methods to fuse and analyze these diverse data sources, enabling more accurate and robust perception under challenging conditions. A key direction is the development of parameter-efficient adaptation frameworks, which can effectively leverage pre-trained models and adapt them to new sensing modalities with minimal labeled data. Another area of focus is the design of efficient and lightweight architectures for multimodal object detection, which can balance performance and computational cost. Furthermore, researchers are working on addressing the scale gap in remote sensing imagery, where tiny objects and large objects coexist, by proposing scale-adaptive and density-guided approaches. Noteworthy papers in this area include UniDiff, which proposes a parameter-efficient framework for adapting diffusion models to multiple sensing modalities, and MM-DETR, which introduces a lightweight and efficient framework for multimodal object detection. Additionally, papers like Bridging the Scale Gap and Edge-Native, Behavior-Adaptive Drone System demonstrate innovative solutions for tiny object detection and wildlife monitoring. Other notable works include Enhancing Cross Domain SAR Oil Spill Segmentation, Leveraging Large-Scale Pretrained Spatial-Spectral Priors, and UAUTrack, which showcase advancements in cross-domain segmentation, pansharpening, and anti-UAV tracking.
Advancements in Multimodal Remote Sensing
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UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotations
MM-DETR: An Efficient Multimodal Detection Transformer with Mamba-Driven Dual-Granularity Fusion and Frequency-Aware Modality Adapters
Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagery
Edge-Native, Behavior-Adaptive Drone System for Wildlife Monitoring
Enhancing Cross Domain SAR Oil Spill Segmentation via Morphological Region Perturbation and Synthetic Label-to-SAR Generation
Leveraging Large-Scale Pretrained Spatial-Spectral Priors for General Zero-Shot Pansharpening
UAUTrack: Towards Unified Multimodal Anti-UAV Visual Tracking
MOS: Mitigating Optical-SAR Modality Gap for Cross-Modal Ship Re-Identification
Label-Efficient Hyperspectral Image Classification via Spectral FiLM Modulation of Low-Level Pretrained Diffusion Features
Difference Decomposition Networks for Infrared Small Target Detection
MKSNet: Advanced Small Object Detection in Remote Sensing Imagery with Multi-Kernel and Dual Attention Mechanisms
Unlocking the Invisible Urban Traffic Dynamics under Extreme Weather: A New Physics-Constrained Hamiltonian Learning Algorithm
Origin-Conditional Trajectory Encoding: Measuring Urban Configurational Asymmetries through Neural Decomposition
Traffic Image Restoration under Adverse Weather via Frequency-Aware Mamba
DisentangleFormer: Spatial-Channel Decoupling for Multi-Channel Vision
Dual-Stream Spectral Decoupling Distillation for Remote Sensing Object Detection
DuGI-MAE: Improving Infrared Mask Autoencoders via Dual-Domain Guidance
Infrared UAV Target Tracking with Dynamic Feature Refinement and Global Contextual Attention Knowledge Distillation
SDG-Track: A Heterogeneous Observer-Follower Framework for High-Resolution UAV Tracking on Embedded Platforms
RAMEN: Resolution-Adjustable Multimodal Encoder for Earth Observation