Special Issue "Advanced Artificial Intelligence and Deep Learning for Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editor

Dr. Gwanggil Jeon

Guest Editor
Department of Embedded Systems Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Korea
Interests: wireless communication; 5G; IoT; artificial intelligence; machine learning; data fusion learning; remote sensing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing is a fundamental tool for comprehending the earth and supporting human-earth communications. Artificial intelligence applications and data science techniques lead new research chances in various fields such as remote sensing, which uses next fifth-generation communications and IoT. In remote sensing area, the system and human generated information bring a massive amount of data while new levels of accuracy, complexity, security, achievement, and reliability are requested. To this end, applicable and consistent research on artificial intelligence and data mining-based methods are needed. These methods can be general and specific tools of artificial intelligence including regression models, neural networks, decision trees, information retrieval, reinforcement learning, graphical models, and decision processes. We trust artificial intelligence and data science methods will provide promising tools to many challenging issues in remote sensing in terms of accuracy and reliability. This Special Issue aims to report the latest advances and trends concerning the advanced artificial learning and data science techniques to the remote sensing data processing issues. Papers of both theoretical and applicative nature, as well as contributions regarding new advanced artificial learning and data science techniques for the remote sensing research community are welcome. Topics of interest mainly include but are not limited to:

  • Artificial intelligence and data science approach for remote sensing
  • Reinforcement learning for remote sensing
  • Information retrieval for remote sensing
  • Big data analytics for beyond 5G
  • Edge/fog computing for remote sensing
  • IoT data analytics in remote sensing
  • Data-driven applications in remote sensing

Prof. Dr. Gwanggil Jeon
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • data science
  • reinforcement learning
  • information retrieval
  • ibg data analytics for beyond 5G
  • edge/fog computing
  • IoT data analytics
  • data-driven applications
  • remote sensing

Published Papers (1 paper)

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Research

Open AccessArticle
AMN: Attention Metric Network for One-Shot Remote Sensing Image Scene Classification
Remote Sens. 2020, 12(24), 4046; https://doi.org/10.3390/rs12244046 - 10 Dec 2020
Abstract
In recent years, deep neural network (DNN) based scene classification methods have achieved promising performance. However, the data-driven training strategy requires a large number of labeled samples, making the DNN-based methods unable to solve the scene classification problem in the case of a [...] Read more.
In recent years, deep neural network (DNN) based scene classification methods have achieved promising performance. However, the data-driven training strategy requires a large number of labeled samples, making the DNN-based methods unable to solve the scene classification problem in the case of a small number of labeled images. As the number and variety of scene images continue to grow, the cost and difficulty of manual annotation also increase. Therefore, it is significant to deal with the scene classification problem with only a few labeled samples. In this paper, we propose an attention metric network (AMN) in the framework of the few-shot learning (FSL) to improve the performance of one-shot scene classification. AMN is composed of a self-attention embedding network (SAEN) and a cross-attention metric network (CAMN). In SAEN, we adopt the spatial attention and the channel attention of feature maps to obtain abundant features of scene images. In CAMN, we propose a novel cross-attention mechanism which can highlight the features that are more concerned about different categories, and improve the similarity measurement performance. A loss function combining mean square error (MSE) loss with multi-class N-pair loss is developed, which helps to promote the intra-class similarity and inter-class variance of embedding features, and also improve the similarity measurement results. Experiments on the NWPU-RESISC45 dataset and the RSD-WHU46 dataset demonstrate that our method achieves the state-of-the-art results on one-shot remote sensing image scene classification tasks. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing)
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