@Fields: Remote sensing, Signal processing, Imaging, SAR, Polarimetry, Tomography, Machine Learning, Deep Learning, Front-end Developer.
@Language: English, Chinese, Persian, Italian (Elementary level).
I have worked in the Remote sensing field for around 10 years. I am a dedicated professional with a strong background in Remote Sensing, Synthetic Aperture Radar (SAR), and Radar technology, With a passion for innovation and a commitment to excellence, I have honed a unique blend of technical expertise and creative problem-solving skills.
My proficiency in Machine Learning and Deep Learning empowers me to create intelligent systems that learn from data, enabling predictive modeling and pattern recognition. I have a track record of designing and implementing machine learning algorithms that contribute to more accurate and efficient data-driven solutions.
Wenyu is a very professional freelancer who not only help me finish my project with high quality but also always keep me posted about the processing with good communication. I feel good to work with her. Highly recommend !
5 ay önce
Beijing Institute of Technology Innovation Center
Ağu 2020 - Ara 2021 (1 yıl, 4 ay)
Lead a group on search, design, develop and manage a softare used in a high-level Ground-based SAR systems.
Ph.D. in Communication and Information Engineering
Università degli Studi di Napoli 'Parthenope', Italy 2021 - 2023
Master in Communication and Information Science
Beijing Institute of Technology, China 2017 - 2020
Bachor In Electronic Engineering
Beijing Institute of Technology, China 2013 - 2017
Three-Dimensional Ground-Based SAR Imaging Algorithm Based on Keystone Formatting and Subblock
To realize 3D displacement monitoring with high spatial resolution and short revisit time, in this paper, a novel 3D imaging algorithm is proposed. Based on characteristics of the model of echo data from the large range and wide-view angle scenario, the proposed method uses keystone formatting to complete range migration correction and subblocks dechirping to realize horizontal focus.
A multi-objective deep learning based approach for SAR image reconstruction in urban environment
IEEE Joint Urban Remote Sensing Event (JURSE)
Urban areas are very challenging to be characterized and usually require a specific filtering design. In this work the result in urban areas of a CNN solution trained on natural scenarios with a realistic dataset, are exploited. The results show a good generalization ability thanks to the wide and realistic dataset and to the multi-objective nature of the designed cost function.
DL BASED FOREST HEIGHT RECONSTRUCTION USING SINGLE-POL TOMOSARIMAGES
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
The aim of this paper is to exploit the potential of deep learning for retrieving forest height by using single polarimetric data, going beyond the limitation of the requirement for full polarization. We design a fully connected network handling the forest height reconstruction problem from a classification task perspective.
A Deep Learning Solution for Height Estimation on a Forested Area based on Pol-TomoSAR data
IEEE Transactions on Geoscience and Remote Sensing (TGRS)
This paper describes a deep learning approach, named Tomographic SAR Neural Network (TSNN), that aims at reconstructing forest and ground height using multipolarimetric multibaseline (MPMB) SAR data and Light Detection and Ranging (LiDAR) based data.
A Deep Learning Solution for Height Inversion on Forested Areas using TomoSAR
IEEE Geoscience and Remote Sensing Letters (GRSL)
The aim of this paper is to go beyond the limitation of the requirement for full polarization by extending, TSNN, a neural network for TomoSAR, to the case of single-polarimetric (SP) and dual-polarimetric (DP) TomoSAR data for retrieving forest height and underlying topography.
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