works on

From the 1 of 12 linked papers with an AI index.

activity
20242026
collaborators

12 papers

cs.CV2026

ReLATE: Reliability-Guided Evidence Fusion for Robust UAV--Satellite cross-view Geo-Localization

Haochen Jiang, Jialei Pan, Yuzhe Sun +4

The paper introduces UAVSat-Deg, a large benchmark for evaluating UAV‑to‑satellite geo‑localization under various image degradations, and proposes ReLATE, a reliability‑guided feat…

cs.CV2026

Learn Temporal Consistency For Robust Satellite Video Detector

Weilong Guo, Shengyang Li, Yanfeng Gu

Satellite video object detection (SVOD) for oriented and fine-grained objects plays an important role in satellite applications. Most existing SVOD methods only focus on one or a f…

cs.CV2026

An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification

Xian Li, Yanfeng, Yanfeng Gu +3

Multispectral point cloud (MPC) is composed of 3D spatial-spectral information, which holds tremendous potential for accurate land-cover classification. However, the representation…

cs.CV2026

TV Subgradient-Guided Multi-Source Fusion for Spectral Imaging in Dual-Camera CASSI Systems

Weiqiang Zhao, Tianzhu Liu, Yuzhe Gui +2

Balancing spectral, spatial, and temporal resolutions is a key challenge in spectral imaging. The Dual-Camera Coded Aperture Snapshot Spectral Imaging (DC-CASSI) system alleviates…

cs.CV2026

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer

Tianlong Ai, Tianzhu Liu, Haochen Jiang +1

Hierarchical land cover and land use (LCLU) classification aims to assign pixel-wise labels with multiple levels of semantic granularity to remote sensing (RS) imagery. However, ex…

cs.CV2026

CroBIM-U: Uncertainty-Driven Referring Remote Sensing Image Segmentation

Yuzhe Sun, Zhe Dong, Haochen Jiang +2

Referring remote sensing image segmentation aims to localize specific targets described by natural language within complex overhead imagery. However, due to extreme scale variation…