Lunar Rock Classification Using Machine Learning

Lunar landings by esteemed space stations around the world have yielded an abundance of new scientific data on the Moon which has helped scientists to study our closest neighbour and hence have provided evidence for understanding Earth’s past and future. This paper is about solving the challenge on HackerEarth about classifying the lunar rock into small or large rock. These tasks have historically been conducted by visual image inspection, thereby reducing the scope, reliability and accuracy of the retrieval. The competition was to build a machine learning model to reduce human effort of doing a monotonous task. We built a Support Vector Machine model, used widely in classification problems, feeding features extracted from images in the dataset using OpenCV, only to obtain an accuracy of 99.41%. Our source code solving the challenge and the dataset are given in the github repository https://github.com/ArshitaKalra/Lunar-Rock-classification.