Name: LiangWenkai
Type: User
Company: Xidian University
Bio: My research interests include the synthetic aperture
radar image analysis and interpretation, deep learning, and
statistical learning theory.
Location: Shaanxi
Blog: https://github.com/liangwenkai
LiangWenkai's Projects
MobileNetV3 SSD的简洁版本
SIRV-Based High-Resolution PolSAR Image Speckle Suppression via Dual-Domain Filtering
Papers with code. Sorted by stars. Updated weekly.
This repository includes functions by Carlos López and Alberto González that enable the interchange of information between Matlab and PolSARPro app in PolSAR data processing
lightweight and efficient cnn for semantic segmentation, my blog address:
a unified convolution on both Euclidean and non-Euclidean domains
Reconstruction full-pol data from single-pol SAR data
This is the source code of ‘Synthetic Aperture Radar Image Generation with Deep Generative Models’ paper
A collection of loss functions for medical image segmentation
Segmentation models with pretrained backbones. Keras.
A light-weight deep learning library based on Caffe
Semantic segmentation of remotely sensing images
常用的语义分割架构结构综述以及代码复现
Utility to access the API of Copernicus Sentinels Scientific Data Hub
[Under cleaning process] Code for Statistically-motivated Second-order Pooling, ECCV2018
GitHub repository for "A Riemannian Network for SPD Matrix Learning", AAAI 2017.
Q. Zhang, Q. Yuan, C. Zeng, X. Li, and Y. Wei, “Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network,” IEEE TGRS, 2018.
本项目收集了计算机类常用电子书整理,附带下载链接,包括Java,Python,Linux,Go,C,C++,数据结构与算法,人工智能,计算机基础,面试,设计模式,数据库,前端、TensorFlow、pytorch、keras。NLP、机器学习,深度学习、大数据系列(Spark,Hadoop,Scala,kafka)等书籍
supervised deep sparse coding networks
Simulation done for TGRS paper: Design of New Wavelet Packets Adapted to High-Resolution SAR Images with an Application to Target Detection
this code implements the method proposed in paper "Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF". if it helps you, please kindly cite this paper。
Model Compression—YOLOv3 with multi lightweight backbones(ShuffleNetV2 HuaWei GhostNet), attention, prune and quantization
YOLOv3 in PyTorch > ONNX > CoreML > TFLite