Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images

Subhankar Roy, Enver Sangineto, Begum Demir, Nicu Sebe
2018 IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium  
The growing volume of Remote Sensing (RS) image archives demands for feature learning techniques and hashing functions which can: (1) accurately represent the semantics in the RS images; and (2) have quasi real-time performance during retrieval. This paper aims to address both challenges at the same time, by learning a semantic-based metric space for content based RS image retrieval while simultaneously producing binary hash codes for an efficient archive search. This double goal is achieved by
more » ... training a deep network using a combination of different loss functions which, on the one hand, aim at clustering semantically similar samples (i.e., images), and, on the other hand, encourage the network to produce final activation values (i.e., descriptors) that can be easily binarized. Moreover, since RS annotated training images are too few to train a deep network from scratch, we propose to split the image representation problem in two different phases. In the first we use a general-purpose, pre-trained network to produce an intermediate representation, and in the second we train our hashing network using a relatively small set of training images. Experiments on two aerial benchmark archives show that the proposed method outperforms previous state-of-theart hashing approaches by up to 5.4% using the same number of hash bits per image. Index Termsdeep hashing, metric learning, content based image retrieval, remote sensing
doi:10.1109/igarss.2018.8518381 dblp:conf/igarss/RoySDS18 fatcat:bmq4flrinbbq7hbtgxewtejosq