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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: closed (30 June 2021) | Viewed by 8683

Special Issue Editor

Dr. Gwanggil Jeon
E-Mail Website
Guest Editor
Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea
Interests: image processing; particularly image compression, motion estimation, demosaicking and image enhancement, and computational intelligence, such as fuzzy and rough sets theories
Special Issues, Collections and Topics 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 submissions that pass pre-check are 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 2500 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 (8 papers)

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Editorial

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Editorial
Editorial for the Special Issue “Advanced Artificial Intelligence and Deep Learning for Remote Sensing”
Remote Sens. 2021, 13(15), 2883; https://doi.org/10.3390/rs13152883 - 23 Jul 2021
Viewed by 635
Abstract
Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications [...] Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing)

Research

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Article
Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning
Remote Sens. 2021, 13(8), 1577; https://doi.org/10.3390/rs13081577 - 19 Apr 2021
Cited by 5 | Viewed by 841
Abstract
The evaporation duct is a weather phenomenon that often occurs in marine environments and affects the operation of shipborne radar. The most important evaluation parameter is the evaporation duct height (EDH). Forecasting the EDH and adjusting the working parameters and modes of the [...] Read more.
The evaporation duct is a weather phenomenon that often occurs in marine environments and affects the operation of shipborne radar. The most important evaluation parameter is the evaporation duct height (EDH). Forecasting the EDH and adjusting the working parameters and modes of the radar system in advance can greatly improve radar performance. Traditionally, short-term forecast methods have been used to estimate the EDH, which are characterized by low time resolution and poor forecast accuracy. In this study, a novel approach for EDH nowcasting is proposed based on the deep learning network and EDH data measured in the Yellow Sea, China. The factors that affect nowcasting were analyzed. The time resolution and forecast time were 5 min and 0–2 h, respectively. The results show that our proposed method has a higher forecast accuracy than traditional time series forecasting methods and confirm its feasibility and effectiveness. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing)
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Article
Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies
Remote Sens. 2021, 13(7), 1234; https://doi.org/10.3390/rs13071234 - 24 Mar 2021
Cited by 7 | Viewed by 1034
Abstract
Vehicle density and technological development increase the need for road and pedestrian safety systems. Identifying problems and addressing them through the development of systems to reduce the number of accidents and loss of life is imperative. This paper proposes the analysis and management [...] Read more.
Vehicle density and technological development increase the need for road and pedestrian safety systems. Identifying problems and addressing them through the development of systems to reduce the number of accidents and loss of life is imperative. This paper proposes the analysis and management of dangerous situations, with the help of systems and modules designed in this direction. The approach and classification of situations that can cause accidents is another feature analyzed in this paper, including detecting elements of a psychosomatic nature: analysis and detection of the conditions a driver goes through, pedestrian analysis, and maintaining a preventive approach, all of which are embedded in a modular architecture. The versatility and usefulness of such a system come through its ability to adapt to context and the ability to communicate with traffic safety systems such as V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2X (vehicle-to-everything), and VLC (visible light communication). All these elements are found in the operation of the system and its ability to become a portable device dedicated to road safety based on (radio frequency) RF-VLC (visible light communication). Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing)
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Article
Multiscale Decomposition Prediction of Propagation Loss in Oceanic Tropospheric Ducts
Remote Sens. 2021, 13(6), 1173; https://doi.org/10.3390/rs13061173 - 19 Mar 2021
Cited by 2 | Viewed by 716
Abstract
The oceanic tropospheric duct is a structure with an abnormal atmospheric refractive index. This structure severely affects the remote sensing detection capability of electromagnetic systems designed for an environment with normal atmospheric refraction. The propagation loss of electromagnetic waves in the oceanic duct [...] Read more.
The oceanic tropospheric duct is a structure with an abnormal atmospheric refractive index. This structure severely affects the remote sensing detection capability of electromagnetic systems designed for an environment with normal atmospheric refraction. The propagation loss of electromagnetic waves in the oceanic duct is an important concept in oceanic duct research. Owing to the long-term stability and short-term irregular changes in marine environmental parameters, the propagation loss in oceanic ducts has nonstationary and multiscale time characteristics. In this paper, we propose a multiscale decomposition prediction method for predicting the propagation loss in oceanic tropospheric ducts. The prediction performance was verified by simulating propagation loss data with noise. Using different evaluation criteria, the experimental results indicated that the proposed method outperforms six other comparison methods. Under noisy conditions, ensemble empirical mode decomposition effectively disassembles the original propagation loss into a limited number of stable sequences with different scale characteristics. Accordingly, predictive modeling was conducted based on multiscale propagation loss characteristic sequences. Finally, we reconstructed the predicted result to obtain the predicted value of the propagation loss in the oceanic duct. Additionally, a genetic algorithm was used to improve the generalization ability of the proposed method while avoiding the nonlinear predictor from falling into a local optimum. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing)
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Article
Continuous Particle Swarm Optimization-Based Deep Learning Architecture Search for Hyperspectral Image Classification
Remote Sens. 2021, 13(6), 1082; https://doi.org/10.3390/rs13061082 - 12 Mar 2021
Cited by 6 | Viewed by 1034
Abstract
Deep convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI) classification. However, the most successful CNN architectures are handcrafted, which need professional knowledge and consume a very significant amount of time. To automatically design cell-based CNN architectures for HSI classification, we [...] Read more.
Deep convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI) classification. However, the most successful CNN architectures are handcrafted, which need professional knowledge and consume a very significant amount of time. To automatically design cell-based CNN architectures for HSI classification, we propose an efficient continuous evolutionary method, named CPSO-Net, which can dramatically accelerate optimal architecture generation by the optimization of weight-sharing parameters. First, a SuperNet with all candidate operations is maintained to share the parameters for all individuals and optimized by collecting the gradients of all individuals in the population. Second, a novel direct encoding strategy is devised to encode architectures into particles, which inherit the parameters from the SuperNet. Then, particle swarm optimization is used to search for the optimal deep architecture from the particle swarm. Furthermore, experiments with limited training samples based on four widely used biased and unbiased hyperspectral datasets showed that our proposed method achieves good performance comparable to the state-of-the-art HSI classification methods. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing)
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Article
Boundary-Assisted Learning for Building Extraction from Optical Remote Sensing Imagery
Remote Sens. 2021, 13(4), 760; https://doi.org/10.3390/rs13040760 - 18 Feb 2021
Cited by 10 | Viewed by 1492
Abstract
Deep learning methods have been shown to significantly improve the performance of building extraction from optical remote sensing imagery. However, keeping the morphological characteristics, especially the boundaries, is still a challenge that requires further study. In this paper, we propose a novel fully [...] Read more.
Deep learning methods have been shown to significantly improve the performance of building extraction from optical remote sensing imagery. However, keeping the morphological characteristics, especially the boundaries, is still a challenge that requires further study. In this paper, we propose a novel fully convolutional network (FCN) for accurately extracting buildings, in which a boundary learning task is embedded to help maintain the boundaries of buildings. Specifically, in the training phase, our framework simultaneously learns the extraction of buildings and boundary detection and only outputs extraction results while testing. In addition, we introduce spatial variation fusion (SVF) to establish an association between the two tasks, thus coupling them and making them share the latent semantics and interact with each other. On the other hand, we utilize separable convolution with a larger kernel to enlarge the receptive fields while reducing the number of model parameters and adopt the convolutional block attention module (CBAM) to boost the network. The proposed framework was extensively evaluated on the WHU Building Dataset and the Inria Aerial Image Labeling Dataset. The experiments demonstrate that our method achieves state-of-the-art performance on building extraction. With the assistance of boundary learning, the boundary maintenance of buildings is ameliorated. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing)
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Article
Combined Geometric Positioning and Performance Analysis of Multi-Resolution Optical Imageries from Satellite and Aerial Platforms Based on Weighted RFM Bundle Adjustment
Remote Sens. 2021, 13(4), 620; https://doi.org/10.3390/rs13040620 - 09 Feb 2021
Cited by 2 | Viewed by 779
Abstract
Combined geometric positioning using images with different resolutions and imaging sensors is being increasingly widely utilized in practical engineering applications. In this work, we attempt to perform the combined geometric positioning and performance analysis of multi-resolution optical images from satellite and aerial platforms [...] Read more.
Combined geometric positioning using images with different resolutions and imaging sensors is being increasingly widely utilized in practical engineering applications. In this work, we attempt to perform the combined geometric positioning and performance analysis of multi-resolution optical images from satellite and aerial platforms based on weighted rational function model (RFM) bundle adjustment without using ground control points (GCPs). Firstly, we introduced an integrated image matching method combining least squares and phase correlation. Next, for bundle adjustment, a combined model of the geometric positioning based on weighted RFM bundle adjustment was derived, and a method for weight determination was given to make the weights of all image points variable. Finally, we conducted experiments using a case study in Shanghai with ZiYuan-3 (ZY-3) satellite imagery, GeoEye-1 satellite imagery, and Digital Mapping Camera (DMC) aerial imagery to validate the effectiveness of the proposed weighted method, and to investigate the positioning accuracy by using different combination scenarios of multi-resolution heterogeneous images. The experimental results indicate that the proposed weighted method is effective, and the positioning accuracy of different combination scenarios can give a good reference for the combined geometric positioning of multi-stereo heterogeneous images in future practical engineering applications. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing)
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Article
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
Cited by 8 | Viewed by 1048
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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