Crop Type Classification using Multi-temporal Sentinel-2 Satellite Imagery: A Deep Semantic Segmentation Approach
Author:
Affiliation:
1. National University of Sciences and Technology (NUST),Islamabad,Pakistan
2. Technical University of Kaiserslautern,Robotics Research Lab,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10089530/10089531/10089586.pdf?arnumber=10089586
Reference33 articles.
1. 10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product
2. Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time;case;Remote Sensing,2019
3. Crop Mapping Using the Historical Crop Data Layer and Deep Neural Networks: A Case Study in Jilin Province, China
4. An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping
5. Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
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1. MCWESRGAN: Improving Enhanced Super-Resolution Generative Adversarial Network for Satellite Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2023
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