Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021.

Overview

Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021.

Bobo Xi, Jiaojiao Li, Yunsong Li and Qian Du.


Code for paper: Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification.

Fig. 1: The framework of our proposed SSGPN for HSI classification.

Training and Test Process

Please simply run 'SSGPN_IP.py' to reproduce the SSGPN results on IndianPines data set. The groundtruth and the obtained classification map are shown below. We have successfully test it on Ubuntu 16.04 with Tensorflow 1.13.1 and GTX 1080 Ti GPU.

Fig. 2: The groundtruth and classification map of Indian Pines dataset.

References

If you find this code helpful, please kindly cite:

[1] B. Xi, J. Li, Y. Li and Q. Du, "Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 2851-2854, doi: 10.1109/IGARSS47720.2021.9553372
[2] B. Xi, J. Li, Y. Li, R. Song, Y. Shi, S. Liu, Q. Du "Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3683-3700, 2020, doi: 10.1109/JSTARS.2020.3004973.

Citation Details

BibTeX entry:

@INPROCEEDINGS{Xi2021IGARSS,
  author={Xi, Bobo and Li, Jiaojiao and Li, Yunsong and Du, Qian},
  booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS}, 
  title={Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification}, 
  year={2021},
  volume={},
  number={},
  pages={2851-2854},
  doi={10.1109/IGARSS47720.2021.9553372}}
@ARTICLE{Xi2020JSTARS,
  author={B. {Xi} and J. {Li} and Y. {Li} and R. {Song} and Y. {Shi} and S. {Liu} and Q. {Du}},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, 
  title={Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification}, 
  year={2020},
  volume={13},
  number={},
  pages={3683-3700},
  doi={10.1109/IGARSS47720.2021.9553372}}

Licensing

Copyright (C) 2020 Bobo Xi

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program.

Owner
Bobo Xi
I‘m a 3rd year Ph. D. candidate from Xidian University, where I am now focusing on hyperspectral image process and deep learning.
Bobo Xi
Establishing Strong Baselines for TripClick Health Retrieval; ECIR 2022

TripClick Baselines with Improved Training Data Welcome 🙌 to the hub-repo of our paper: Establishing Strong Baselines for TripClick Health Retrieval

Sebastian Hofstätter 3 Nov 03, 2022
A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers.

ViTGAN: Training GANs with Vision Transformers A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers. Refer

Hong-Jia Chen 127 Dec 23, 2022
This Artificial Intelligence program can take a black and white/grayscale image and generate a realistic or plausible colorized version of the same picture.

Colorizer The point of this project is to write a program capable of taking a black and white / grayscale image, and generating a realistic or plausib

Maitri Shah 1 Jan 06, 2022
VD-BERT: A Unified Vision and Dialog Transformer with BERT

VD-BERT: A Unified Vision and Dialog Transformer with BERT PyTorch Code for the following paper at EMNLP2020: Title: VD-BERT: A Unified Vision and Dia

Salesforce 44 Nov 01, 2022
Framework to build and train RL algorithms

RayLink RayLink is a RL framework used to build and train RL algorithms. RayLink was used to build a RL framework, and tested in a large-scale multi-a

Bytedance Inc. 32 Oct 07, 2022
Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

Information Gain Filtration Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF sho

4 Jul 28, 2022
Decorator for PyMC3

sampled Decorator for reusable models in PyMC3 Provides syntactic sugar for reusable models with PyMC3. This lets you separate creating a generative m

Colin 50 Oct 08, 2021
Continual learning with sketched Jacobian approximations

Continual learning with sketched Jacobian approximations This repository contains the code for reproducing figures and results in the paper ``Provable

Machine Learning and Information Processing Laboratory 1 Jun 30, 2022
nextPARS, a novel Illumina-based implementation of in-vitro parallel probing of RNA structures.

nextPARS, a novel Illumina-based implementation of in-vitro parallel probing of RNA structures. Here you will find the scripts necessary to produce th

Jesse Willis 0 Jan 20, 2022
Anime Face Detector using mmdet and mmpose

Anime Face Detector This is an anime face detector using mmdetection and mmpose. (To avoid copyright issues, I use generated images by the TADNE model

198 Jan 07, 2023
The code used for the free [email protected] Webinar series on Reinforcement Learning in Finance

Reinforcement Learning in Finance [email protected] Webinar This repository provides the code f

Yves Hilpisch 62 Dec 22, 2022
UniFormer - official implementation of UniFormer

UniFormer This repo is the official implementation of "Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning". It curren

SenseTime X-Lab 573 Jan 04, 2023
Rewrite ultralytics/yolov5 v6.0 opencv inference code based on numpy, no need to rely on pytorch

Rewrite ultralytics/yolov5 v6.0 opencv inference code based on numpy, no need to rely on pytorch; pre-processing and post-processing using numpy instead of pytroch.

炼丹去了 21 Dec 12, 2022
TextureGAN in Pytorch

TextureGAN This code is our PyTorch implementation of TextureGAN [Project] [Arxiv] TextureGAN is a generative adversarial network conditioned on sketc

Patsorn 147 Dec 14, 2022
Multi-View Radar Semantic Segmentation

Multi-View Radar Semantic Segmentation Paper Multi-View Radar Semantic Segmentation, ICCV 2021. Arthur Ouaknine, Alasdair Newson, Patrick Pérez, Flore

valeo.ai 37 Oct 25, 2022
Implementation of DropLoss for Long-Tail Instance Segmentation in Pytorch

[AAAI 2021]DropLoss for Long-Tail Instance Segmentation [AAAI 2021] DropLoss for Long-Tail Instance Segmentation Ting-I Hsieh*, Esther Robb*, Hwann-Tz

Tim 37 Dec 02, 2022
Fast EMD for Python: a wrapper for Pele and Werman's C++ implementation of the Earth Mover's Distance metric

PyEMD: Fast EMD for Python PyEMD is a Python wrapper for Ofir Pele and Michael Werman's implementation of the Earth Mover's Distance that allows it to

William Mayner 433 Dec 31, 2022
Morphable Detector for Object Detection on Demand

Morphable Detector for Object Detection on Demand (ICCV 2021) PyTorch implementation of the paper Morphable Detector for Object Detection on Demand. I

9 Feb 23, 2022
Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch

Reminder ST-GCN has transferred to MMSkeleton, and keep on developing as an flexible open source toolbox for skeleton-based human understanding. You a

sijie yan 1.1k Dec 25, 2022
A Python package for faster, safer, and simpler ML processes

Bender 🤖 A Python package for faster, safer, and simpler ML processes. Why use bender? Bender will make your machine learning processes, faster, safe

Otovo 6 Dec 13, 2022