A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features

A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features

Land cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep learning (DL), particularly convolutional neural networks (CNNs) and vision transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. In this wor...

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Bibliographic Details
Main Authors: Luigi Russo, Antonietta Sorriso, Silvia Liberata Ullo, Paolo Gamba
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural network (CNN)
deep learning (DL)
land cover (LC) mapping
neural network
Sentinel-1 (S1)
swin-Unet
Online Access:https://ieeexplore.ieee.org/document/10948270/
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