Eagle-Eyed Multitask CNNs for Aerial Image Retrieval and Scene Classification

Abstract

In recent years, convolutional neural networks (CNNs) have become the predominant method for content-based aerial image retrieval (CBAIR) and aerial scene classification (ASC) due to their overwhelming performance advantages. However, existing CNN-based models have the following shortcomings: first, they do not deal with large intraclass variations, thereby overlooking the possibility of fine-grained retrieval and classification; second, all similarity learning methods for CBAIR consider similarity between two images as a constant, neglecting the fact that image similarity is uncertain in nature; third, similarity learning is separated from ASC, ignoring the advantages of joint optimization. To address these issues, we propose a novel metric learning method called center-metric learning, and couple it with a new kind of loss called positive-negative center loss, which, with the help of several “experts,” enables CNNs to cope successfully with within-class variations. Besides, we propose similarity distribution learning, making the first attempt to embed uncertainty regarding similarity into the training process. The resulting fine-grained similarity predictions can further strengthen CNNs’ fine discrimination ability. Furthermore, three tasks, that is, center-metric learning, similarity distribution learning, and ASC, are incorporated into one CNN, benefitting from one another and leading to a better generalization capability. Just like an eagle, our model is able to discriminate subtle differences among aerial images, hence the name “eagle-eyed multitask CNN.” We carry out extensive experiments over four publicly available aerial image sets and achieve a performance better than all existing methods.