Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new...

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Bibliographic Details
Main Authors: Vittorio Mazzia, Aleem Khaliq, Marcello Chiaberge
Format: Article
Language:English
Published: MDPI AG 2019-12-01
Series:Applied Sciences
Subjects:
satellite imagery
deep learning
pixel-based crops classification
recurrent neural networks
convolutional neural networks
Online Access:https://www.mdpi.com/2076-3417/10/1/238

Internet

https://www.mdpi.com/2076-3417/10/1/238

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