Few Shot Metric Learning for Remote Sensing Image Scene Classification

Abstract

Visual recognition in aerial imagery plays an important role in a wide range of applications such as surveillance, monitoring, detection and management of natural and manmade disasters. Current advances in deep learning show promising results for computer vision tasks of classification, detection, segmentation and tracking. The meta-learning branch of deep learning seeks to train models that learn new concepts with few labeled examples, to save data annotation cost and solve the problem of data scarcity. Recent works show the superior performance of metric-learning approaches for meta-learning among others. This research evaluates two state-of-the-art metric-learning methods, namely Prototypical Networks and Relation Networks, in remote sensing imagery and explores avenues to improve performance by utilizing efficient networks with different depths for feature extraction and jointly training on multi-domain data. The performance of the same efficient networks is also evaluated for object detection in satellite imagery, to aid in the wise selection of feature extraction backbone for a meta-learning object detector. Our results suggest that Prototypical Networks are faster to train and more accurate than Relation Networks when the number of training classes are limited. Furthermore, jointly training on natural and satellite imagery for few shot classification is shown to slightly improve accuracy, given a suitable feature extraction backbone. Finally, we conclude that MobileNet v2 might serve as the potential network design to begin design space exploration of feature extraction backbones targeted for accurate and efficient meta-learning as it outperforms its competitors in both the tasks of object detection and few shot classification. xiii