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Electronics
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Deep Learning in Image Processing and Pattern Recognition, 2nd Edition
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Deep Learning in Image Processing and Pattern Recognition, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 648

Special Issue Editors

Prof. Dr. Yuji Iwahori

E-Mail Website
Guest Editor
Department of Computer Science, Chubu University, 1200 Matsumoto-cho, Kasugai 487-8501, Aichi, Japan
Interests: computer vision; neural networks; machine learning; medical image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Aili Wang

E-Mail Website
Guest Editor
Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China
Interests: remote sensing image processing; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haibin Wu

E-Mail Website
Guest Editor
Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China
Interests: machine vision; visual detection and image processing; medical virtual reality
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoming Sun

E-Mail Website
Guest Editor
Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China
Interests: machine vision; visual detection and image processing; medical virtual reality

Special Issue Information

Dear Colleagues, 

People primarily use images to acquire and exchange information, so the application of image processing is inevitably involved in all aspects of human life and work. At present, image processing technology has played an important role in the fields of aerospace, public security, biomedicine, industrial engineering, and business communication. Up until now, image processing technology based on deep learning has rapidly developed and become the most successful applied intelligent technology. Pattern recognition is an important research field in image processing and includes image preprocessing, feature extraction and selection, classifier design, and classification decisions.

In this context, for this Special Issue on “Deep Learning in Image Processing and Pattern Recognition”, we invite original research and comprehensive reviews on topics that include, but are not limited to, the following:

  • Advances in image preprocessing;
  • Advances in feature selection in images;
  • Advances in pattern recognition in image processing technology;
  • Image processing in intelligent transportation;
  • Hyperspectral image processing;
  • Biomedical image processing;
  • Image processing in intelligent monitoring;
  • Deep learning for image processing;
  • AI-based image processing, understanding, recognition, compression, and reconstruction.

Prof. Dr. Yuji Iwahori
Dr. Aili Wang
Prof. Dr. Haibin Wu
Dr. Xiaoming Sun
Guest Editors

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. Electronics 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

  • deep learning
  • image processing
  • pattern recognition

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Published Papers (1 paper)

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Research

20 pages, 22781 KiB  
Article
Multi-Scale Residual Spectral–Spatial Attention Combined with Improved Transformer for Hyperspectral Image Classification
by Aili Wang, Kang Zhang, Haibin Wu, Yuji Iwahori and Haisong Chen
Electronics 2024, 13(6), 1061; https://doi.org/10.3390/electronics13061061 - 13 Mar 2024
Viewed by 511
Abstract
Aiming to solve the problems of different spectral bands and spatial pixels contributing differently to hyperspectral image (HSI) classification, and sparse connectivity restricting the convolutional neural network to a globally dependent capture, we propose a HSI classification model combined with multi-scale residual spectral–spatial [...] Read more.
Aiming to solve the problems of different spectral bands and spatial pixels contributing differently to hyperspectral image (HSI) classification, and sparse connectivity restricting the convolutional neural network to a globally dependent capture, we propose a HSI classification model combined with multi-scale residual spectral–spatial attention and an improved transformer in this paper. First, in order to efficiently highlight discriminative spectral–spatial information, we propose a multi-scale residual spectral–spatial feature extraction module that preserves the multi-scale information in a two-layer cascade structure, and the spectral–spatial features are refined by residual spectral–spatial attention for the feature-learning stage. In addition, to further capture the sequential spectral relationships, we combine the advantages of Cross-Attention and Re-Attention to alleviate computational burden and attention collapse issues, and propose the Cross-Re-Attention mechanism to achieve an improved transformer, which can efficiently alleviate the heavy memory footprint and huge computational burden of the model. The experimental results show that the overall accuracy of the proposed model in this paper can reach 98.71%, 99.33%, and 99.72% for Indiana Pines, Kennedy Space Center, and XuZhou datasets, respectively. The proposed method was verified to have high accuracy and effectiveness compared to the state-of-the-art models, which shows that the concept of the hybrid architecture opens a new window for HSI classification. Full article
(This article belongs to the Special Issue Deep Learning in Image Processing and Pattern Recognition, 2nd Edition)
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