Remote Sensing Image Classification

Overview

Transfer learning applied to train an image classifier for classifying remote sensing data into three classes:

• aircrafts
• ships
• none

Table of Contents

• Installation
• Dataset
• Running

Installation

The program requires the following dependencies (easy to install using pip: pip3 install -r requirements.txt):

• python 3.5
• pytorch
• numpy
• pandas
• matplotlib
• Pillow
• CUDA (for using GPU)

Dataset

The dataset can be downloaded or used from here
After downloading it can be extracted by:

unzip src.zip

The structure of extracted folder is shown below:

src
├── test [121 entries]
│   ├── testing.csv
├── train
│   ├── aircrafts [500 entries]
│   ├── none [500 entries]
│   ├── ships [500 entries]
│   └── training.csv
├── main.py
├── utils_jnb.py
└── utils.py

Running

To train the model, simply run python3 main.py.
Once trained, you can test the results with python3 main.py --test True (make sure that you have a saved model file : model.pt before testing)

Here are some flags which could be useful. For more help and options, use python3 main.py -h:

• --directory : if the current directory is not src.
• --batch : to change the training batch size (default = 32)
• --epochs : to change the number of epochs (default = 25)
• --val : to change the fraction of validation set out of total training set (default = 0.1)

(or) Simply run the jupyter notebook file in google colab (make sure that the src.zip file is in right place)