Late fusion of pre-trained networks for satellite image classification

Abstract

This paper presents a deep learning approach to image classification in satellite imagery based on late fusion in conjunction with pre-trained networks. The pre-trained models are especially useful for image classification and can be used as the backbone for transfer learning. The intuition behind transfer learning is that these pre-trained models will effectively serve as a generic model of the visual world. This paper addresses the problem of object classification in representative data limited environment and exploits the pre-trained networks in conjunction with late fusion to perform classification on satellite images. Interestingly, the pre-trained networks namely ResNet50 and VGG16 trained on ImageNet (a large collection of photographs), and yet yield results with high accuracy on satellite images. The experimental results show that the late fusion method outperforms the other competing approaches buy a considerable margin of over 10 percentage points.