collaborators

9 papers

cs.CV2026

Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment

Ziyao Wang, Maonan Wang, Yucheng He +5

Cloud removal (CR) is essential for optical remote sensing, serving as a prerequisite for reliable downstream interpretation, such as semantic segmentation and change detection. Ho…

eess.IV2026

Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction

Hao Yang, Xianping Ma, Peifeng Ma +1

High-resolution remote sensing imagery is critical for environmental monitoring, urban mapping, and land cover analysis, but its transmission is often hindered by limited bandwidth…

cs.CV2026

MPerS: Dynamic MLLM MixExperts Perception-Guided Remote Sensing Scene Segmentation

Ziyi Wang, Xianping Ma, Ziyao Wang +2

The multimodal fusion of images and scene captions has been extensively explored and applied in various fields. However, when dealing with complex remote sensing (RS) scenes, exist…

cs.CV2026

Open-Vocabulary Semantic Segmentation Network Integrating Object-Level Label and Scene-Level Semantic Features for Multimodal Remote Sensing Images

Jinkun Dai, Yuanxin Ye, Peng Tang +4

Semantic segmentation of multi-modal remote sensing imagery plays a pivotal role in land use/land cover (LULC) mapping, environmental monitoring, and precision earth observation. C…

cs.MM2026

Geo2Sound: A Scalable Geo-Aligned Framework for Soundscape Generation from Satellite Imagery

Kunlin Wu, Yanning Wang, Haofeng Tan +6

Recent image-to-audio models have shown impressive performance on object-centric visual scenes. However, their application to satellite imagery remains limited by the complex, wide…

cs.CV2025

A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation

Xianping Ma, Xiaokang Zhang, Man-On Pun +1

Multimodal remote sensing data, acquired from diverse sensors, offer a comprehensive and integrated perspective of the Earth's surface. Leveraging multimodal fusion techniques, sem…