13 papers
MGRL-RSCC: Multi-Granularity Reward Reinforcement Learning for Fine-Grained Remote Sensing Change Captioning
Futian Wang, Mengqi Wang, Xiao Wang +4
Remote Sensing Change Captioning (RSCC), which aims to generate accurate and detailed linguistic descriptions of ground object variations from bi-temporal remote sensing images, is…
Graph-based Semantic Calibration Network for Unaligned UAV RGBT Image Semantic Segmentation and A Large-scale Benchmark
Fangqiang Fan, Zhicheng Zhao, Xiaoliang Ma +2
Fine-grained RGBT image semantic segmentation is crucial for all-weather unmanned aerial vehicle (UAV) scene understanding. However, UAV RGBT image semantic segmentation faces two…
Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark
Jinquan Yan, Zhicheng Zhao, Zhengzheng Tu +3
UAV images are critical for applications such as large-area mapping, infrastructure inspection, and emergency response. However, in real-world flight environments, a single image i…
UAV traffic scene understanding: A regulation embedded multi-modal network and a unified benchmark
Yu Zhang, Zhicheng Zhao, Ze Luo +2
Traffic scene understanding from unmanned aerial vehicle (UAV) platforms is crucial for intelligent transportation systems due to its flexible deployment and wide-area monitoring c…
Physics-Constrained Cross-Resolution Enhancement Network for Optics-Guided Thermal UAV Image Super-Resolution
Zhicheng Zhao, Fengjiao Peng, Jinquan Yan +3
Optics-guided thermal UAV image super-resolution has attracted significant research interest due to its potential in all-weather monitoring applications. However, existing methods…
Learning Where to Focus: Density-Driven Guidance for Detecting Dense Tiny Objects
Zhicheng Zhao, Xuanang Fan, Lingma Sun +2
High-resolution remote sensing imagery increasingly contains dense clusters of tiny objects, the detection of which is extremely challenging due to severe mutual occlusion and limi…
Towards Robust Optical-SAR Object Detection under Missing Modalities: A Dynamic Quality-Aware Fusion Framework
Zhicheng Zhao, Yuancheng Xu, Andong Lu +2
Optical and Synthetic Aperture Radar (SAR) fusion-based object detection has attracted significant research interest in remote sensing, as these modalities provide complementary in…
Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagery
Zhicheng Zhao, Yin Huang, Lingma Sun +2
Tiny object detection in remote sensing imagery has attracted significant research interest in recent years. Despite recent progress, achieving balanced detection performance acros…
DialBench: Towards Accurate Reading Recognition of Pointer Meter using Large Foundation Models
Futian Wang, Chaoliu Weng, Xiao Wang +3
The precise reading recognition of pointer meters plays a key role in smart power systems, but existing approaches remain fragile due to challenges like reflections, occlusions, dy…
DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model
Zhicheng Zhao, Jinquan Yan, Chenglong Li +2
Optical remote sensing image dehazing presents significant challenges due to its extensive spatial scale and highly non-uniform haze distribution, which traditional single-image de…
Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering
Zhicheng Zhao, Changfu Zhou, Yu Zhang +3
Remote Sensing Visual Question Answering (RSVQA) has gained significant research interest. However, current RSVQA methods are limited by the imaging mechanisms of optical sensors,…
Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception
Xiao Wang, Wentao Wu, Chenglong Li +4
Understanding vehicles in images is important for various applications such as intelligent transportation and self-driving system. Existing vehicle-centric works typically pre-trai…
Physics Inspired Hybrid Attention for SAR Target Recognition
Zhongling Huang, Chong Wu, Xiwen Yao +3
There has been a recent emphasis on integrating physical models and deep neural networks (DNNs) for SAR target recognition, to improve performance and achieve a higher level of phy…