Sea Ice Remote Sensing

Authors: Sangwon Lim and Omar Kawach

Purpose: Submission for the group project in University of Victoria's Artificial Intelligence course (ECE 470).

Description: Get a model and see if it can be applicable to other data.

Summary of the Project

• Sea ice concentration classification models generated using Deep Learning architectures
• Utilized Gray Level Co-occurrence Matrix (GLCM) products for feature engineering
• Devised the training and test data splitting strategy to mitigate the spatial auto-correlation in training
• Utilized 1D-CNN to generate convolved features for maximized relationships between optical bands
• Devised a deep learning architecture concatenating Multi-layer Neural Network and 1D-CNN
• Identified the optimum feature selections, architectures and classification scheme for the problem
• Model assessments based on confusion matrices, accuracy and F1 score
• 47.65% accuracy on 8-class classification comparable to existing 2D-CNN model’s 55.3% that was tested without consideration of spatial auto-correlation

Getting Started

Install the Package in a Python Virtual Environment

To avoid conflicts, the first step is to isolate this project by creating a Python virtual environment called venv. The virtual environment will have it's own python interpreter, dependencies, and scripts. Commands should only be entered in a terminal that has venv active.

MacOS / Linux

python -m venv venv
source venv/bin/activate
pip install .
pip install -r requirements.txt

Windows

python -m venv venv
venv/Scripts/activate
pip install .
pip install -r requirements.txt

Getting the Data

For our research we used optical data from Kaggle. The Python programs in the package were built with the following dataset:

Sylvester, S. (2021, April). Arctic sea ice image masking, Version 3. Retrieved May 17, 2021

To use the dataset we selected, ensure that you have a Kaggle API token properly saved locally.

Once you have ensured that you have a Kaggle API token, cd into the data folder and run the following command:

kaggle datasets download alexandersylvester/arctic-sea-ice-image-masking 

Running Programs

For the workflow, the commands below are in sequential order. Again, make sure you are in venv when running these commands.

Note: If you are on Windows, be sure to write python before the path to the script you are trying to run. Only Windows requires a relative path to the script you are trying to run. The commands below assume you are in the project's home directory.

1. Extracting X-Y Coordinates of Patch Locations

Purpose: Preprocessing step for feature extraction.

Note: The reference image of patch locations is retrieved using the extract_patch_locations shell script or batch script.

Unix-like Operating Systems

./extract_patch_locations.sh

Windows

./extract_patch_locations.bat

2. Pixel Based Feature Extraction

Purpose: Features for machine learning should be extracted for each sample pixel. Extracts pixel samples where the number of samples per class is nearly consistent throughout the resulting dataset.

Distribution statistics

Unix-like Operating Systems

Command to run distribution statistics on a folder:

dist-stat --input data/arctic-sea-ice-image-masking/Masks

Command to run distribution statistics on a single file:

dist-stat --input data/arctic-sea-ice-image-masking/Masks/P0-2016042417-mask.png

Windows

Command to run distribution statistics on a folder:

python scripts/dist-stat --input data/arctic-sea-ice-image-masking/Masks

Command to run distribution statistics on a single file:

python scripts/dist-stat --input data/arctic-sea-ice-image-masking/Masks/P0-2016042417-mask.png

Create datasets

Unix-like Operating Systems

Command to create datasets via multiprocessing:

create-datasets --images ./data/arctic-sea-ice-image-masking/Images --masks ./data/arctic-sea-ice-image-masking/Masks --dist ./data/pixel_values.csv --patch-loc ./data/AOIs_R_thresh_CL_centroids.csv --multiprocess

Command to create datasets without multiprocessing:

create-datasets --images ./data/arctic-sea-ice-image-masking/Images --masks ./data/arctic-sea-ice-image-masking/Masks --dist ./data/pixel_values.csv --dist ./data/pixel_values.csv --patch-loc ./data/AOIs_R_thresh_CL_centroids.csv

Windows

Note: WinError 5 will occur if you try creating datasets with multiprocessing on Windows.

Command to create datasets without multiprocessing:

python scripts/create-datasets --images ./data/arctic-sea-ice-image-masking/Images --masks ./data/arctic-sea-ice-image-masking/Masks --dist ./data/pixel_values.csv --dist ./data/pixel_values.csv --patch-loc ./data/AOIs_R_thresh_CL_centroids.csv

3. Generate GLCM Texture Features

Purpose: Generate 5 GLCM products for each of the data points

Unix-like Operating Systems

GLCM --input ./data/train_dataset/raw.csv --img-dir ./data/arctic-sea-ice-image-masking/Images

Windows

python scripts/GLCM --input ./data/train_dataset/raw.csv --img-dir ./data/arctic-sea-ice-image-masking/Images

4. Normalize Data

Purpose: Normalizing data can result in better performance of the model. Except for training data, the strategy of normalization should include standard min & max values instead of calculating such values within the dataset. The standard values are from the training dataset.

Unix-like Operating Systems

To normalize the training dataset:

normalize --input ./data/train_dataset/GLCM.csv --std-data ./data/train_dataset/GLCM.csv

To normalize the test dataset:

normalize --input ./data/test_dataset/GLCM.csv --std-data ./data/train_dataset/GLCM.csv

Windows

To normalize the training dataset:

python scripts/normalize --input ./data/train_dataset/GLCM.csv --std-data ./data/train_dataset/GLCM.csv

To normalize the test dataset:

python scripts/normalize --input ./data/test_dataset/GLCM.csv --std-data ./data/train_dataset/GLCM.csv

5. Machine Learning

Purpose: Training, testing, and predicting of the model.

Note: The commands below only seem to work on MacOS with M1 chip.

Neural Network

Train neural network:

neural-network --dl-config ./DL_configs/GLCM_C6_cat.yml

CNN

Train 1D-CNN (To concatenate multi-layer neural network, add features other than spectral data and GLCM products):

# 1D-CNN
CNN --dl-config ./DL_configs/GLCM_C6.yml

# Concatenation of 1D-CNN and multi-layer NN
CNN --dl-config ./DL_configs/GLCM_C6_cat.yml

Test Model

Test the model:

test-model --dl-config ./DL_configs/GLCM_C6_cat.yml --result-dir ./results/CNN_GLCM_C6_cat

Predict

For an image, run a prediction:

predict --patch-loc ./data/AOIs_R_thresh_CL_centroids.csv --std-data ./data/train_dataset/GLCM.csv --result-dir ./results/CNN_GLCM_C4_cat/ckpt_1 --dl-config ./DL_configs/GLCM_C4_cat.yml --mask-dir ./data/arctic-sea-ice-image-masking/Masks --input ./data/arctic-sea-ice-image-masking/Images/P54-2018071616.jpg --classes 4

Figure 1. Expert Image

Figure 2. Prediction Image

Sources

[1] R. Ressel, A. Frost and S. Lehner, "A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 7, pp. 3672-3680, July 2015, doi: 10.1109/JSTARS.2015.2436993.