Early-Season Crop Mapping on an Agricultural Area in Italy Using <i>X</i>-Band Dual-Polarization SAR Satellite Data and Convolutional Neural Networks-Reference-Cited by-同舟云学术

Early-Season Crop Mapping on an Agricultural Area in Italy Using X-Band Dual-Polarization SAR Satellite Data and Convolutional Neural Networks

Published:2022 Issue: Volume:15 Page:6789-6803
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Fontanelli Giacomo¹^ORCID,Lapini Alessandro¹^ORCID,Santurri Leonardo¹^ORCID,Pettinato Simone¹^ORCID,Santi Emanuele¹^ORCID,Ramat Giuliano¹^ORCID,Pilia Simone¹^ORCID,Baroni Fabrizio¹^ORCID,Tapete Deodato²^ORCID,Cigna Francesca²^ORCID,Paloscia Simonetta¹^ORCID

Affiliation:

1. Institute of Applied Physics N. Carrara, National Research Council, Florence, Italy

2. Italian Space Agency, Rome, Italy

Funder

Italian Space Agency

Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9656571/09864054.pdf?arnumber=9864054

Reference99 articles.

1. Crop classification using multi-configuration SAR data in the North China Plain

2. Discrimination of crop types with TerraSAR-X-derived information

3. The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data to Crop Classification

4. Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

5. Comparison of Object and Pixel-Based Classifications For Mapping Crops Using Rapideye Imagery: A Case Study Of Menemen Plain, Turkey

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