https://github.com/elbeejay/synthswir

Estimating 2 SWIR bands from the RGB and NIR bands using a simple Neural Network, trained on pixels of Landsat data
https://github.com/elbeejay/synthswir

Last synced: 3 months ago
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Estimating 2 SWIR bands from the RGB and NIR bands using a simple Neural Network, trained on pixels of Landsat data

• Host: GitHub
• URL: https://github.com/elbeejay/synthswir
• Owner: elbeejay
• License: mit
• Created: 2020-04-16T16:34:28.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2021-02-15T17:46:55.000Z (over 4 years ago)
• Last Synced: 2025-01-07T13:26:37.943Z (4 months ago)
• Language: Python
• Homepage:
• Size: 5.68 MB
• Stars: 1
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# SynthSWIR

Estimating SWIR-1 and SWIR-2 bands from the RGB and NIR bands using a regression model trained pixel by pixel.















SynthSWIR is an auxiliary Tensorflow 2 model designed to pre-process [PlanetScope](https://www.planet.com/) and other high resolution satellite data without shortwave infrared (SWIR) so that they may be used with the deep convolutional neural networks [DeepWaterMap](https://github.com/isikdogan/deepwatermap) and [DeepRiver](https://github.com/isikdogan/deepriver). With SynthSWIR, these powerful neural networks can be extended to higher resolution data that lacks the Landsat SWIR-1 and SWIR-2 bands.

As an auxiliary model, SynthSWIR attempts to approximate the SWIR-1 and SWIR-2 bands present in LANDSAT data. This model was designed to help augment RGB+NIR satellite data so that it can be used with the DeepWaterMap/DeepRiver neural networks. Due to their large training datasets (1 TB+) and robust traning methods, it is computationally costly to attempt to re-train DeepWaterMap/DeepRiver with fewer input bands. The advantage of using SynthSWIR, is that the pre-trained DeepWaterMap/DeepRiver models can be applied directly to 4-band (RGB+NIR) data without re-training.

## Contents

  - [Dependencies](#dependencies)

  - [Quick Start](#quick-start)

  - [Model Architecture](#model-architecture)

  - [Pre-Trained Model](#pre-trained-model)

  - [Training On Your Own Data](#training-on-your-own-data)

## Dependencies

*Tested on Python 3.8.2*

  - tensorflow 2+

  - numpy

  - pandas

  - gdal

## Quick Start

From the command line:

  1. Clone the repository: `git clone https://github.com/elbeejay/SynthSWIR`

  2. To apply the model and prediction synthetic SWIR band data for a Landsat GeoTIF, run:

  ```

  python apply_model.py --file_name 'your_landsat_img.tif'

  ```

  3. To apply the model to a PlanetScope GeoTIF, run:

  ```

  python apply_model_planet.py --file_name 'your_planetscope_img.tif'

  ```

The output GeoTIF from the model will be in the same directory as the input image with the suffix *"_predicted"*.

## Model Architecture

SynthSWIR employs a relatively simply methodology and attempts to learn the relationship between RGB+NIR data and the SWIR-1 and SWIR-2 band data on a pixel-by-pixel basis. The model consists of 4 hidden layers as shown in the model schematic below.















Model performance and reduction in loss as training occurs can be seen in the [model_info](model_info) subdirectory. Other model architectures that were tested and their associated error metrics are provided there as well.

## Pre-Trained Model

A pre-trained model and checkpoint have been created using primarily coastal river data. The training data used for this is available in the [training_data](training_data) subdirectory. These pixel values provided in the `.csv` file were extracted from a set of Landsat geoTIFs (available [here](https://utexas.box.com/s/t67iptubwdpvyims0afutiipv8qqrqg5)).

## Training On Your Own Data

To train the model on a different set of Landsat data (highly recommended if the intended use is not for coastal river systems), then do the following.

  1. Collect your Landsat GeoTIFs into a single folder

  2. Run the function `gen_data.create_training(file_dir)` contained in the `gen_data.py` script to create a new `training_data.csv` file with a selection of pixels from your training GeoTIFs. The number of pixels sampled from each GeoTIF in the training data can be specified using the `num_pts` argument.

  3. Run `trainer.py` to train the model on your newly generated `training_data.csv`. Check the script and your working directory to ensure that the `training_data.csv` file being pointed to is the new one you just generated.

  4. Now you have a newly trained model and a checkpoint should have been saved in the `/checkpoints` directory. When SynthSWIR is next applied via `apply_model.py` or `apply_model_planet.py` it will be using the model trained on your training dataset!

## Misc/Other Scripts

The relationship between the SWIR bands and the RGB+NIR bands can be seen using `visualizing_data.py`. This script uses the seaborn package to produce pair-wise plots showing the relationship between each RGB+NIR band and the SWIR-1 and SWIR-2 bands.

To see how well the model does in your area of interest, the `test_model.py` script may be helpful. The function `test_model.predict_comparison(file_name)` takes a Landsat GeoTIF as input, applies the SynthSWIR model, and creates scatter plots of the predicted SWIR values against the true Landsat SWIR values.