NORMA eResearch @NCI Library

Cohesion Intensive Deep Hashing for Remote Sensing Image Retrieval

Han, Lirong, Li, Peng, Bai, Xiao, Grecos, Christos, Zhang, Xiaoyu and Ren, Peng (2020) Cohesion Intensive Deep Hashing for Remote Sensing Image Retrieval. Remote Sensing, 12 (1). p. 101. ISSN 2072-4292

[img]
Preview
PDF
Available under License Creative Commons Attribution Share Alike.

Download (1MB) | Preview
Official URL: http://dx.doi.org/10.3390/rs12010101

Abstract

Recently, the demand for remote sensing image retrieval is growing and attracting the interest of many researchers because of the increasing number of remote sensing images. Hashing, as a method of retrieving images, has been widely applied to remote sensing image retrieval. In order to improve hashing performance, we develop a cohesion intensive deep hashing model for remote sensing image retrieval. The underlying architecture of our deep model is motivated by the state-of-the-art residual net. Residual nets aim at avoiding gradient vanishing and gradient explosion when the net reaches a certain depth. However, different from the residual net which outputs multiple class-labels, we present a residual hash net that is terminated by a Heaviside-like function for binarizing remote sensing images. In this scenario, the representational power of the residual net architecture is exploited to establish an end-to-end deep hashing model. The residual hash net is trained subject to a weighted loss strategy that intensifies the cohesiveness of image hash codes within one class. This effectively addresses the data imbalance problem normally arising in remote sensing image retrieval tasks. Furthermore, we adopted a gradualness optimization method for obtaining optimal model parameters in order to favor accurate binary codes with little quantization error. We conduct comparative experiments on large-scale remote sensing data sets such as UCMerced and AID. The experimental results validate the hypothesis that our method improves the performance of current remote sensing image retrieval.

Item Type: Article
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Staff Research and Publications
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 28 Feb 2020 11:26
Last Modified: 28 Feb 2020 11:26
URI: http://norma.ncirl.ie/id/eprint/4130

Actions (login required)

View Item View Item