Satellite Image Classification with Deep Learning

TL;DR

This paper presents a deep learning system using convolutional neural networks for classifying objects in high-resolution satellite images, achieving high accuracy and competitive performance in a major dataset challenge.

Contribution

It introduces an ensemble of CNNs combined with metadata integration for satellite image classification, advancing automation in geospatial analysis.

Findings

Achieved 83% overall accuracy on the fMoW dataset

Classified 15 classes with over 95% accuracy

Placed 2nd in the fMoW TopCoder competition

Abstract

Satellite imagery is important for many applications including disaster response, law enforcement, and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic expanses to be covered are great and the analysts available to conduct the searches are few, automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that have shown promise for the automation of such tasks. It has achieved success in image understanding by means of convolutional neural networks. In this paper we apply them to the problem of object and facility recognition in high-resolution, multi-spectral satellite imagery. We describe a deep learning system for classifying objects and facilities from the IARPA Functional Map of the World (fMoW) dataset into 63 different classes. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. It is implemented in Python using the Keras and TensorFlow deep learning libraries and runs on a Linux server with an NVIDIA Titan X graphics card. At the time of writing the system is in 2nd place in the fMoW TopCoder competition. Its total accuracy is 83%, the F1 score is 0.797, and it classifies 15 of the classes with accuracies of 95% or better.