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

Hang Yu, Weidong Song (), Bing Zhang, Hongbo Zhu, Jiguang Dai and Jichao Zhang
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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
Bing Zhang: School of Mapping and Geoscience, 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
Jichao Zhang: School of Mapping and Geoscience, Liaoning Technical University, Fuxin 123000, China

Land, 2024, vol. 13, issue 11, 1-18

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.

Keywords: land cover classification; remote sensing image extraction; image stitching; multi-scale feature fusion; local energy features; Gaussian and Laplacian pyramids (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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