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Detection of invasive plants using hyperspectral imagery

Description

This package is intended for running deep learning classifiers on hyperspectral data for mapping invasive alien plants. Models are pretrained using self-supervision and/or noisy land cover label data and then fine-tuned using point labels.

Currently this package is under heavy development

Development roadmap

Current and planned data sources, models and module features are indicated below

Data sources

• Sentinel 2
• Hyperspectral

Models

• TempCNN
• ViT
• Masked Autoencoder

Module features

• Logging with W&B
• Hyperparameter tuning with W&B sweeps

Getting started

First, install dependencies

# clone project   
git clone https://github.com/GMoncrieff/hyper-iap

# install project   
cd hyper-iap  
pip install -r requirements.txt

Next, train classifiers using the command line.

python train.py --model_class=vit.simpleVIT   

For a full list of command line options run

python train.py --help

Imports

You can also import individual modules and incorporate them into python workflows

from lightning import Trainer, seed_everything
from hyperiap.models.vit import simpleVIT
from hyperiap.datasets.xarray_module import XarrayDataModule
from hyperiap.litmodels.litclassifier import LitClassifier

xmod = XarrayDataModule()
model = LitClassifier(simpleVIT(data_config=xmod.config()))
trainer = Trainer(limit_train_batches=5, limit_val_batches=3, max_epochs=2)
trainer.fit(model, datamodule=xmod)
trainer.validate(datamodule=xmod)

Acknowledgements

The module builds on contributions and implementations from :

• TempCNN - Pelletier et al., 2019
• BreizhCrops - Rußwurm et al., 2019
• ViT - Dosovitskiy et al., 2021
• SpectralFormer - Hong et al., 2021
• Full Stack Deep Learning
• xbatcher
• zen3geo

The land cover labels used from pre-training from

• The South African Department of Forestry, Fisheries and the Environment