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  1. Article ; Online: Advanced Machine Learning and Deep Learning Approaches for Remote Sensing

    Gwanggil Jeon

    Remote Sensing, Vol 15, Iss 2876, p

    2023  Volume 2876

    Abstract: Unlike field observation or field sensing, remote sensing is the process of obtaining information about an object or phenomenon without making physical contact [.] ...

    Abstract Unlike field observation or field sensing, remote sensing is the process of obtaining information about an object or phenomenon without making physical contact [.]
    Keywords n/a ; Science ; Q
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Editorial for the Special Issue “Advanced Artificial Intelligence and Deep Learning for Remote Sensing”

    Gwanggil Jeon

    Remote Sensing, Vol 13, Iss 2883, p

    2021  Volume 2883

    Abstract: Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications [.] ...

    Abstract Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications [.]
    Keywords n/a ; Science ; Q
    Language English
    Publishing date 2021-07-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Information Entropy Algorithms for Image, Video, and Signal Processing

    Gwanggil Jeon

    Entropy, Vol 23, Iss 926, p

    2021  Volume 926

    Abstract: Information entropy is a basic concept in information theory associated with any random variable [.] ...

    Abstract Information entropy is a basic concept in information theory associated with any random variable [.]
    Keywords n/a ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Language English
    Publishing date 2021-07-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  4. Article ; Online: Computing Techniques for Environmental Research and Public Health

    Gwanggil Jeon / Abdellah Chehri

    International Journal of Environmental Research and Public Health, Vol 18, Iss 9851, p

    2021  Volume 9851

    Abstract: Human bodies are continuously generating information about our health [.] ...

    Abstract Human bodies are continuously generating information about our health [.]
    Keywords n/a ; Medicine ; R
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Scene Changes Understanding Framework Based on Graph Convolutional Networks and Swin Transformer Blocks for Monitoring LCLU Using High-Resolution Remote Sensing Images

    Sihan Yang / Fei Song / Gwanggil Jeon / Rui Sun

    Remote Sensing, Vol 14, Iss 3709, p

    2022  Volume 3709

    Abstract: High-resolution remote sensing images with rich land surface structure can provide data support for accurately understanding more detailed change information of land cover and land use (LCLU) at different times. In this study, we present a novel scene ... ...

    Abstract High-resolution remote sensing images with rich land surface structure can provide data support for accurately understanding more detailed change information of land cover and land use (LCLU) at different times. In this study, we present a novel scene change understanding framework for remote sensing which includes scene classification and change detection. To enhance the feature representation of images in scene classification, a robust label semantic relation learning (LSRL) network based on EfficientNet is presented for scene classification. It consists of a semantic relation learning module based on graph convolutional networks and a joint expression learning framework based on similarity. Since the bi-temporal remote sensing image pairs include spectral information in both temporal and spatial dimensions, land cover and land use change monitoring can be improved by using the relationship between different spatial and temporal locations. Therefore, a change detection method based on swin transformer blocks (STB-CD) is presented to obtain contextual relationships between targets. The experimental results on the LEVIR-CD, NWPU-RESISC45, and AID datasets demonstrate the superiority of LSRL and STB-CD over other state-of-the-art methods.
    Keywords high-resolution remote sensing images ; LCLU ; scene change understanding ; label semantic relation ; change detection ; transformer ; Science ; Q
    Subject code 006 ; 004
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Entropy-Based Algorithms for Signal Processing

    Gwanggil Jeon / Abdellah Chehri

    Entropy, Vol 22, Iss 621, p

    2020  Volume 621

    Abstract: Entropy, the key factor of information theory, is one of the most important research areas in computer science [.] ...

    Abstract Entropy, the key factor of information theory, is one of the most important research areas in computer science [.]
    Keywords multichannel imaging ; sensor ; dynamic range ; modeling of signal processing ; compression approach ; entropy-based video coding ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Language English
    Publishing date 2020-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: GPU-Accelerated Computation of EM Scattering of a Time-Evolving Oceanic Surface Model II

    Longxiang Linghu / Jiaji Wu / Zhensen Wu / Gwanggil Jeon / Tao Wu

    Remote Sensing, Vol 14, Iss 2727, p

    EM Scattering of Actual Oceanic Surface

    2022  Volume 2727

    Abstract: Based on marine environmental factors of different sea areas, a high-performance sea clutter time series modeling algorithm for the real sea surface is developed to study the amplitude mean and Doppler spectrum characteristics of sea clutter. The ... ...

    Abstract Based on marine environmental factors of different sea areas, a high-performance sea clutter time series modeling algorithm for the real sea surface is developed to study the amplitude mean and Doppler spectrum characteristics of sea clutter. The European Centre for Medium-Range Weather Forecasts (ECMWF) data set (ERA-Interim) and ESA’s soil moisture and ocean salinity (SMOS) data set are utilized to establish databases of different marine environmental factors. Combined with the mixed spectrum model, the geometric fine structure of wind-driven sea surface with swell superposition is established by using the double-superposition method (DSM) and comprehensively considering small-scale capillary ripples, large-scale gravity waves and swell. A triangle facet-based sea clutter series modeling algorithm is developed, in which the quasi-specular scattering based on a triangle and the scattering based on gravity wave modulation capillary spectrum are calculated, respectively, and compared with the measured results. For high-resolution radar, dynamic sea surface modeling and sea clutter calculation are very time consuming. In this paper, the Tesla K80 GPU manufactured by NVIDIA in Santa Clara, Computed Unified Device architecture (CUDA) high-performance parallel technology and some optimization strategies are adopted to improve the efficiency of sea clutter modeling. The results can be used to analyze the distribution characteristics of marine factors, the average amplitude and Doppler characteristics of sea clutter in different sea areas.
    Keywords marine environmental elements ; different sea areas ; real sea surface ; sea clutter characteristics ; CUDA ; Science ; Q
    Subject code 551
    Language English
    Publishing date 2022-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  8. Article ; Online: Hybrid-Scale Hierarchical Transformer for Remote Sensing Image Super-Resolution

    Jianrun Shang / Mingliang Gao / Qilei Li / Jinfeng Pan / Guofeng Zou / Gwanggil Jeon

    Remote Sensing, Vol 15, Iss 3442, p

    2023  Volume 3442

    Abstract: Super-resolution (SR) technology plays a crucial role in improving the spatial resolution of remote sensing images so as to overcome the physical limitations of spaceborne imaging systems. Although deep convolutional neural networks have achieved ... ...

    Abstract Super-resolution (SR) technology plays a crucial role in improving the spatial resolution of remote sensing images so as to overcome the physical limitations of spaceborne imaging systems. Although deep convolutional neural networks have achieved promising results, most of them overlook the advantage of self-similarity information across different scales and high-dimensional features after the upsampling layers. To address the problem, we propose a hybrid-scale hierarchical transformer network (HSTNet) to achieve faithful remote sensing image SR. Specifically, we propose a hybrid-scale feature exploitation module to leverage the internal recursive information in single and cross scales within the images. To fully leverage the high-dimensional features and enhance discrimination, we designed a cross-scale enhancement transformer to capture long-range dependencies and efficiently calculate the relevance between high-dimension and low-dimension features. The proposed HSTNet achieves the best result in PSNR and SSIM with the UCMecred dataset and AID dataset. Comparative experiments demonstrate the effectiveness of the proposed methods and prove that the HSTNet outperforms the state-of-the-art competitors both in quantitative and qualitative evaluations.
    Keywords super-resolution ; remote sensing image ; convolutional neural network ; transformer ; self-similarity ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2023-07-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  9. Article ; Online: Editorial for the Special Issue “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”

    Gwanggil Jeon / Valerio Bellandi / Abdellah Chehri

    Remote Sensing, Vol 12, Iss 2815, p

    2020  Volume 2815

    Abstract: This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In ... ...

    Abstract This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In principal, this edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms. This issue is intended to provide a highly recognized international forum to present recent advances in time series remote sensing. After review, a total of eight papers have been accepted for publication in this issue.
    Keywords time series remote sensing ; data processing ; machine learning ; transfer learning ; cross-sensor learning ; image processing ; Science ; Q
    Language English
    Publishing date 2020-08-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Multi-Scale Mixed Attention Network for CT and MRI Image Fusion

    Yang Liu / Binyu Yan / Rongzhu Zhang / Kai Liu / Gwanggil Jeon / Xiaoming Yang

    Entropy, Vol 24, Iss 843, p

    2022  Volume 843

    Abstract: Recently, the rapid development of the Internet of Things has contributed to the generation of telemedicine. However, online diagnoses by doctors require the analyses of multiple multi-modal medical images, which are inconvenient and inefficient. Multi- ... ...

    Abstract Recently, the rapid development of the Internet of Things has contributed to the generation of telemedicine. However, online diagnoses by doctors require the analyses of multiple multi-modal medical images, which are inconvenient and inefficient. Multi-modal medical image fusion is proposed to solve this problem. Due to its outstanding feature extraction and representation capabilities, convolutional neural networks (CNNs) have been widely used in medical image fusion. However, most existing CNN-based medical image fusion methods calculate their weight maps by a simple weighted average strategy, which weakens the quality of fused images due to the effect of inessential information. In this paper, we propose a CNN-based CT and MRI image fusion method (MMAN), which adopts a visual saliency-based strategy to preserve more useful information. Firstly, a multi-scale mixed attention block is designed to extract features. This block can gather more helpful information and refine the extracted features both in the channel and spatial levels. Then, a visual saliency-based fusion strategy is used to fuse the feature maps. Finally, the fused image can be obtained via reconstruction blocks. The experimental results of our method preserve more textual details, clearer edge information and higher contrast when compared to other state-of-the-art methods.
    Keywords convolutional neural network ; image fusion ; attention ; visual saliency ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Subject code 006
    Language English
    Publishing date 2022-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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