The field of remote sensing and surveillance is witnessing significant advancements with the integration of multimodal learning techniques. Researchers are exploring new paradigms that combine vision, language, and other modalities to improve the accuracy and robustness of remote sensing image classification, object detection, and surveillance systems. Notably, the development of frequency-aware vision-language multimodality generalization networks and multimodal transformer approaches are enabling more effective cross-modal alignment and feature fusion. Furthermore, active perception paradigms and adaptive cropping-zooming frameworks are being proposed to enhance the processing of ultra-high-resolution remote sensing images.

Some noteworthy papers in this area include: Frequency-Aware Vision-Language Multimodality Generalization Network for Remote Sensing Image Classification, which proposes a frequency-aware vision-language multimodality generalization network for remote sensing image classification. MMSense: Adapting Vision-based Foundation Model for Multi-task Multi-modal Wireless Sensing, which integrates image, radar, LiDAR, and textual data into a unified feature space for wireless sensing tasks. ZoomEarth: Active Perception for Ultra-High-Resolution Geospatial Vision-Language Tasks, which presents an adaptive cropping-zooming framework for ultra-high-resolution remote sensing image processing. A Multimodal Transformer Approach for UAV Detection and Aerial Object Recognition Using Radar, Audio, and Video Data, which demonstrates a multimodal transformer model for UAV detection and aerial object recognition. FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding, which proposes a fine-grained aligned RS language-image pretraining framework for remote sensing understanding.