Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery

Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery

Abstract In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale...

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Bibliographic Details
Main Authors: Leo Thomas Ramos, Angel D. Sappa
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Image segmentation
Semantic segmentation
Multispectral imaging
Land cover classification
Remote sensing
Computer vision
Online Access:https://doi.org/10.1038/s41598-024-84795-1

Internet

https://doi.org/10.1038/s41598-024-84795-1

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