LiangWenkai's repositories
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为15个章节，近20万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
SAR-Colorization
Reconstruction full-pol data from single-pol SAR data
SAR_GENERATIVE_MODEL
This is the source code of ‘Synthetic Aperture Radar Image Generation with Deep Generative Models’ paper
semanticSegmentation
Semantic segmentation of remotely sensing images
Wavelet-Packets-for-High-Resolution-SAR-images
Simulation done for TGRS paper: Design of New Wavelet Packets Adapted to High-Resolution SAR Images with an Application to Target Detection
3DMMasSTN
MatConvNet implementation for incorporating a 3D Morphable Model (3DMM) into a Spatial Transformer Network (STN)
Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
ClassifierToolbox
A MATLAB toolbox for classifier: Version 1.0.7
covariancetoolbox
Covariance toolbox for matlab, including riemannian geometry
dcgan-matconvnet
Deep Convolutional Generative Adversarial Network (DCGAN) implementation on MatConvNet (compliant to any MCN version)
demo_MCMs
MCMs for TGRS 2018
Fine-tuning-a-pre-trained-CNN-for-first-year-sea-ice-and-multi-year-sea-ice-cp-imagery-classificatio
Mapping first-year sea ice and multi-year sea ice in the oceans is significant for many applications. For example, ship navigation and weather forecast. Accurate and robust classification methods of multi-year ice and first-year ice are in demand [2]. Hybrid-polarity SAR architecture will be included in future SAR missions such as the Canadian RADARSAT Constellation Mission (RCM). These sensors will enable the use of compact polarimetry (CP) data in wide swath imagery [1]. Convolutional neural networks (CNNs) are becoming increasingly popular in many research communities due to availability of large image datasets and high-performance computing systems. As Convolutional networks (ConvNets) have achieved great success on many image classification tasks, I pursue this method for the classification of image patches from compact polarimety (CP) imagery into first-year ice and multi-year ice is applicable. In this course project, my work is kind of like the first practice of the CP imagery classification by fine-tuning a pre-trained convolutional neural network (CNN). Specifically, fine-tuning the last fully-connected layer of a pre-trained convolutional networks, I extract patches from simulated CP images as my dataset, the classification accuracy of the test set achieved 91.3% by fine-tuning a pre-trained CNN, compared to 49.4% classification accuracy by training from scratch.
GWPF_PolSAR_detetion
One saliency detection method for PolSAR image based on GLOBALLY WEIGHTED PERTURBATION FILTERS
Interview-Notebook
:books: 技术面试需要掌握的基础知识整理
matconvnet-calvin
Code for several state-of-the-art papers in object detection and semantic segmentation.
pwc
Papers with code. Sorted by stars. Updated weekly.
Read-Write-MatlabPolSARPro
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
SACNNs
a unified convolution on both Euclidean and non-Euclidean domains
SegModel
A light-weight deep learning library based on Caffe
sentinelsat
Utility to access the API of Copernicus Sentinels Scientific Data Hub
smsop
[Under cleaning process] Code for Statistically-motivated Second-order Pooling, ECCV2018
SPDNet
GitHub repository for "A Riemannian Network for SPD Matrix Learning", AAAI 2017.
supervised-deep-sparse-coding-networks
supervised deep sparse coding networks
Weakly-Supervised-Segmentation-of-SAR-Imagery-Using-Superpixel-and-Hierarchically-Adversarial-CRF
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。