activity
20222024
most citedOn the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data

7 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Image Fusion in Remote Sensing: An Overview and Meta Analysis

Hessah Albanwan, Rongjun Qin, Yang Tang

Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, an…

cs.CV20237 cited

On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data

Rongjun Qin, Guixiang Zhang, Yang Tang

Recent deep learning-based methods outperform traditional learning methods on remote sensing (RS) semantic segmentation/classification tasks. However, they require large training d…

cs.CV2023

Mesh Conflation of Oblique Photogrammetric Models using Virtual Cameras and Truncated Signed Distance Field

Shuang Song, Rongjun Qin

Conflating/stitching 2.5D raster digital surface models (DSM) into a large one has been a running practice in geoscience applications, however, conflating full-3D mesh models, such…

cs.CV2023

Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets

Mostafa Elhashash, Rongjun Qin

Many practical systems for image-based surface reconstruction employ a stereo/multi-stereo paradigm, due to its ability to scale for large scenes and its ease of implementation for…

cs.LG2023

Scaling Multi-Objective Security Games Provably via Space Discretization Based Evolutionary Search

Yu-Peng Wu, Hong Qian, Rong-Jun Qin +2

In the field of security, multi-objective security games (MOSGs) allow defenders to simultaneously protect targets from multiple heterogeneous attackers. MOSGs aim to simultaneousl…

cs.LG20221 cited

Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games

Rong-Jun Qin, Fan-Ming Luo, Hong Qian +1

This paper addresses policy learning in non-stationary environments and games with continuous actions. Rather than the classical reward maximization mechanism, inspired by the idea…