Next Article in Journal
Using Very High Resolution Thermal Infrared Imagery for More Accurate Determination of the Impact of Land Cover Differences on Evapotranspiration in an Irrigated Agricultural Area
Next Article in Special Issue
A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds
Previous Article in Journal
Enhancement of Component Images of Multispectral Data by Denoising with Reference
Previous Article in Special Issue
Utilizing Multilevel Features for Cloud Detection on Satellite Imagery
Open AccessArticle

Description Generation for Remote Sensing Images Using Attribute Attention Mechanism

1
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
2
Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
3
Computer Science Department, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 612; https://doi.org/10.3390/rs11060612
Received: 31 January 2019 / Revised: 3 March 2019 / Accepted: 9 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Analysis of Big Data in Remote Sensing)
Image captioning generates a semantic description of an image. It deals with image understanding and text mining, which has made great progress in recent years. However, it is still a great challenge to bridge the “semantic gap” between low-level features and high-level semantics in remote sensing images, in spite of the improvement of image resolutions. In this paper, we present a new model with an attribute attention mechanism for the description generation of remote sensing images. Therefore, we have explored the impact of the attributes extracted from remote sensing images on the attention mechanism. The results of our experiments demonstrate the validity of our proposed model. The proposed method obtains six higher scores and one slightly lower, compared against several state of the art techniques, on the Sydney Dataset and Remote Sensing Image Caption Dataset (RSICD), and receives all seven higher scores on the UCM Dataset for remote sensing image captioning, indicating that the proposed framework achieves robust performance for semantic description in high-resolution remote sensing images. View Full-Text
Keywords: remote sensing image captioning; attributes; attention mechanism; convolutional neural network; long short-term memory network remote sensing image captioning; attributes; attention mechanism; convolutional neural network; long short-term memory network
Show Figures

Graphical abstract

MDPI and ACS Style

Zhang, X.; Wang, X.; Tang, X.; Zhou, H.; Li, C. Description Generation for Remote Sensing Images Using Attribute Attention Mechanism. Remote Sens. 2019, 11, 612.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop