Slice-feature based deep hashing algorithm for remote sensing image retrieval

Abstract

Remote sensing image retrieval is aimed at obtaining a target image from a typically large number of remote sensing images. Different from natural scene images, remote sensing images usually includes more channels such as near-infrared reflectances (NIR), which will result in higher data dimensionality. However, high dimensionality of image features may lead to information redundancy and high computational costs. Recently, deep hashing methods to reduce image feature dimensionality have been proposed. However, compared with natural images, remote sensing images have smaller interclass distances, which hinder the application of deep hashing methods for image retrieval. To overcome this problem, in this paper, we develop a slice-feature deep hashing (SFDH) method for remote sensing image retrieval. The major contribution of this work is proposing an image correlation reduction strategy by separating the features in the fully connected layers into many slices. The SFDH architecture is based on a pre-trained Inception V3 model; the proposed model not only extracts image features from remote sensing images, but also introduces the slice-feature strategy to improve the fully connected structure of the previously proposed metric learning based deep hashing network (MiLaN). Besides, triplet loss is utilized to further enlarge differences between classes. Our experimental results demonstrate that the proposed slice-feature strategy outperforms state-of-the-art remote sensing image retrieval methods.