Search Results for author:
Found 9 papers, 6 papers with code
OASL: Orientation-aware adaptive sampling learning for arbitrary oriented object detection
Arbitrary oriented object detection (AOOD) is a fundamental task in aeiral image interpretation, which is commonly implemented by optimizing three subtasks: classification, localization, and orientation.
ABFL: Angular Boundary Discontinuity Free Loss for Arbitrary Oriented Object Detection in Aerial Images
Existing methods lack intuitive modeling of angle difference measurement in oriented Bbox representations.
A Lightweight Recurrent Aggregation Network for Satellite Video Super-Resolution
In the context of RASVSR, with better alignment and fusion, we make the perceptual field of each frame spanning 100 frames of the video, thus acquiring richer information, and information between different images can be complementary.
Ranked #1 on
Video Super-Resolution
on SAT-MTB-VSR
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
Planetary UAV localization based on Multi-modal Registration with Pre-existing Digital Terrain Model
In this paper, we proposed a multi-modal registration based SLAM algorithm, which estimates the location of a planet UAV using a nadir view camera on the UAV compared with pre-existing digital terrain model.
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation.
CNNTOP: a CNN-based Trajectory Owner Prediction Method
In this paper, we propose a novel CNN-based Trajectory Owner Prediction (CNNTOP) method.
How Agile is the Adaptive Data Rate Mechanism of LoRaWAN?
The LoRaWAN based Low Power Wide Area networks aim to provide long-range connectivity to a large number of devices by exploiting limited radio resources.
Networking and Internet Architecture
Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models
The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models.
