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Applied Sciences
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Application of Machine Learning to Image Classification and Image Segmentation
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Application of Machine Learning to Image Classification and Image Segmentation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1756

Special Issue Editor

Prof. Dr. Fan Liu

E-Mail Website
Guest Editor
Department of Computer Science and Technology, College of Computer and Information, Hohai University, Nanjing 210098, China
Interests: computer vision; pattern recognition; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image classification and image segmentation are two important tasks in the field of computer vision. Image classification aims to distinguish different categories of targets based on the different features reflected in the image information. It uses computers to quantitatively analyze images, categorizing each pixel or region in the image into one of several categories. Image segmentation refers to the process of subdividing a digital image into multiple image subregions that have certain similarities in features, while there are significant differences between different subregions. The goal of image segmentation is to assign a category label to each pixel in the image, achieving a fine understanding of the image. In recent decades, deep learning techniques have made unprecedented advancements in both image classification and image segmentation. Despite promising performance with existing methods, they are still challenged with numerous open issues. This organized Special Issue endeavors to show the new developments in both image classification and image segmentation to highlight future research.

Prof. Dr. Fan Liu
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. Applied Sciences 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

  • image classification
  • image segmentation
  • deep learning
  • machine learning

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Published Papers (2 papers)

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Research

21 pages, 4590 KiB  
Article
Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
by Girma Tariku, Isabella Ghiglieno, Andres Sanchez Morchio, Luca Facciano, Celine Birolleau, Anna Simonetto, Ivan Serina and Gianni Gilioli
Appl. Sci. 2025, 15(2), 871; https://doi.org/10.3390/app15020871 - 17 Jan 2025
Viewed by 723
Abstract
Land cover mapping is essential to understanding global land-use patterns and studying biodiversity composition and the functioning of eco-systems. The introduction of remote sensing technologies and artificial intelligence models made it possible to base land cover mapping on satellite imagery in order to [...] Read more.
Land cover mapping is essential to understanding global land-use patterns and studying biodiversity composition and the functioning of eco-systems. The introduction of remote sensing technologies and artificial intelligence models made it possible to base land cover mapping on satellite imagery in order to monitor changes, assess ecosystem health, support conservation efforts, and reduce monitoring time. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, including a lack of comparative studies for the selection of optimal deep learning models. No less important is the scarcity of aerial datasets specifically tailored for agricultural areas. This study addresses these gaps by presenting a methodology for semantic segmentation of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the “Land Cover Aerial Imagery” (LICAI) dataset for semantic segmentation. The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conducted a comparative study, using cutting-edge deep-learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately identifying land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpretability is essential for advancing the field of remote sensing, in support of biodiversity conservation and the provision of ecosystem services and sustainable agriculture. Full article
(This article belongs to the Special Issue Application of Machine Learning to Image Classification and Image Segmentation)
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16 pages, 8947 KiB  
Article
Research on Personnel Image Segmentation Based on MobileNetV2 H-Swish CBAM PSPNet in Search and Rescue Scenarios
by Di Zhao, Weiwei Zhang and Yuxing Wang
Appl. Sci. 2024, 14(22), 10675; https://doi.org/10.3390/app142210675 - 19 Nov 2024
Viewed by 625
Abstract
In post-disaster search and rescue scenarios, the accurate image segmentation of individuals is essential for efficient resource allocation and effective rescue operations. However, challenges such as image blur and limited resources complicate personnel segmentation. This paper introduces an enhanced, lightweight version of the [...] Read more.
In post-disaster search and rescue scenarios, the accurate image segmentation of individuals is essential for efficient resource allocation and effective rescue operations. However, challenges such as image blur and limited resources complicate personnel segmentation. This paper introduces an enhanced, lightweight version of the Pyramid Scene Parsing Network (MHC-PSPNet). By substituting ResNet50 with the more efficient MobileNetV2 as the model backbone, the computational complexity is significantly reduced. Furthermore, replacing the ReLU6 activation function in MobileNetV2 with H-Swish enhances segmentation accuracy without increasing the parameter count. To further amplify high-level semantic features, global pooled features are fed into an attention mechanism network. The experimental results demonstrate that MHC-PSPNet performs exceptionally well on our custom dataset, achieving 97.15% accuracy, 89.21% precision, an F1 score of 94.53%, and an Intersection over Union (IoU) of 83.82%. Compared to the ResNet50 version, parameters are reduced by approximately 18.6 times, while detection accuracy improves, underscoring the efficiency and practicality of the proposed algorithm. Full article
(This article belongs to the Special Issue Application of Machine Learning to Image Classification and Image Segmentation)
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