Inference and discovery in remote sensing data with features extracted using deep networks

Abstract

We aim to develop a process by which we can extract generic features from aerial image data that can both be used to infer the presence of objects and characteristics and to discover new ways of representing the landscape. We investigate the fine-tuning of a 50-layer ResNet deep convolutional neural network that was pre-trained with ImageNet data and extracted features at several layers throughout these pre-trained and the fine-tuned networks. These features were applied to several supervised classification problems, obtaining a significant correlation between the classification accuracy and layer number. Visualising the activation of the networks’ nodes found that fine-tuning had not achieved coherent representations at later layers. We conclude that we need to train with considerably more varied data but that, even without fine tuning, features derived from a deep network can produce better classification results than with image data alone.