activity
20212024
most cited-Net: Superresolving SAR Tomographic Inversion via Deep Learning

57 citations · 128 across the 10 of their papers we have counts for

collaborators

10 papers

cs.LG20242 cited

Large-scale flood modeling and forecasting with FloodCast

Qingsong Xu, Yilei Shi, Jonathan Bamber +2

Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to a…

cs.CV2023

HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images

Sining Chen, Yilei Shi, Zhitong Xiong +1

3D geo-information is of great significance for understanding the living environment; however, 3D perception from remote sensing data, especially on a large scale, is restricted. T…

cs.CV2023

Self-supervised Domain-agnostic Domain Adaptation for Satellite Images

Fahong Zhang, Yilei Shi, Xiao Xiang Zhu

Domain shift caused by, e.g., different geographical regions or acquisition conditions is a common issue in machine learning for global scale satellite image processing. A promisin…

cs.CV20231 cited

Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects

Fahong Zhang, Yilei Shi, Zhitong Xiong +1

Object detection is an essential and fundamental task in computer vision and satellite image processing. Existing deep learning methods have achieved impressive performance thanks…

cs.CV20231 cited

UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation

Qingsong Xu, Yilei Shi, Jianhua Guo +2

Change detection (CD) by comparing two bi-temporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervis…

cs.CV20233 cited

High Quality Large-Scale 3-D Urban Mapping with Multi-Master TomoSAR

Yilei Shi, Richard Bamler, Yuanyuan Wang +1

Multi-baseline interferometric synthetic aperture radar (InSAR) techniques are effective approaches for retrieving the 3-D information of urban areas. In order to obtain a plausibl…