IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i11p1842-d1514574.html
   My bibliography  Save this article

MMS-EF: A Multi-Scale Modular Extraction Framework for Enhancing Deep Learning Models in Remote Sensing

Author

Listed:
  • Hang Yu

    (School of Mapping and Geoscience, Liaoning Technical University, Fuxin 123000, China)

  • Weidong Song

    (School of Mapping and Geoscience, Liaoning Technical University, Fuxin 123000, China
    Collaborative Innovation Institute of Geospatial Information Service, Liaoning Technical University, Fuxin 123000, China)

  • Bing Zhang

    (School of Mapping and Geoscience, Liaoning Technical University, Fuxin 123000, China
    Collaborative Innovation Institute of Geospatial Information Service, Liaoning Technical University, Fuxin 123000, China)

  • Hongbo Zhu

    (School of Mapping and Geoscience, Liaoning Technical University, Fuxin 123000, China)

  • Jiguang Dai

    (School of Mapping and Geoscience, Liaoning Technical University, Fuxin 123000, China
    Collaborative Innovation Institute of Geospatial Information Service, Liaoning Technical University, Fuxin 123000, China)

  • Jichao Zhang

    (School of Mapping and Geoscience, Liaoning Technical University, Fuxin 123000, China)

Abstract

The analysis of land cover using deep learning techniques plays a pivotal role in understanding land use dynamics, which is crucial for land management, urban planning, and cartography. However, due to the complexity of remote sensing images, deep learning models face practical challenges in the preprocessing stage, such as incomplete extraction of large-scale geographic features, loss of fine details, and misalignment issues in image stitching. To address these issues, this paper introduces the Multi-Scale Modular Extraction Framework (MMS-EF) specifically designed to enhance deep learning models in remote sensing applications. The framework incorporates three key components: (1) a multiscale overlapping segmentation module that captures comprehensive geographical information through multi-channel and multiscale processing, ensuring the integrity of large-scale features; (2) a multiscale feature fusion module that integrates local and global features, facilitating seamless image stitching and improving classification accuracy; and (3) a detail enhancement module that refines the extraction of small-scale features, enriching the semantic information of the imagery. Extensive experiments were conducted across various deep learning models, and the framework was validated on two public datasets. The results demonstrate that the proposed approach effectively mitigates the limitations of traditional preprocessing methods, significantly improving feature extraction accuracy and exhibiting strong adaptability across different datasets.

Suggested Citation

  • Hang Yu & Weidong Song & Bing Zhang & Hongbo Zhu & Jiguang Dai & Jichao Zhang, 2024. "MMS-EF: A Multi-Scale Modular Extraction Framework for Enhancing Deep Learning Models in Remote Sensing," Land, MDPI, vol. 13(11), pages 1-18, November.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1842-:d:1514574
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/11/1842/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/13/11/1842/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lin Luo & Pengpeng Li & Xuesong Yan, 2021. "Deep Learning-Based Building Extraction from Remote Sensing Images: A Comprehensive Review," Energies, MDPI, vol. 14(23), pages 1-25, November.
    2. Agathos Filintas & Nikolaos Gougoulias & Nektarios Kourgialas & Eleni Hatzichristou, 2023. "Management Soil Zones, Irrigation, and Fertigation Effects on Yield and Oil Content of Coriandrum sativum L. Using Precision Agriculture with Fuzzy k-Means Clustering," Sustainability, MDPI, vol. 15(18), pages 1-33, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chunhai Tan & Tao Chen & Jiayu Liu & Xin Deng & Hongfei Wang & Junwei Ma, 2024. "Building Extraction from Unmanned Aerial Vehicle (UAV) Data in a Landslide-Affected Scattered Mountainous Area Based on Res-Unet," Sustainability, MDPI, vol. 16(22), pages 1-15, November.
    2. Andreas Braun & Gebhard Warth & Felix Bachofer & Michael Schultz & Volker Hochschild, 2023. "Mapping Urban Structure Types Based on Remote Sensing Data—A Universal and Adaptable Framework for Spatial Analyses of Cities," Land, MDPI, vol. 12(10), pages 1-41, October.
    3. Maria Spyridoula Tzima & Athos Agapiou & Vasiliki Lysandrou & Georgios Artopoulos & Paris Fokaides & Charalambos Chrysostomou, 2023. "An Application of Machine Learning Algorithms by Synergetic Use of SAR and Optical Data for Monitoring Historic Clusters in Cypriot Cities," Energies, MDPI, vol. 16(8), pages 1-20, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1842-:d:1514574. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.