Machine Learning-Based Approach for Tillage Identification Using Sentinel-1 Data-Reference-Cited by-同舟云学术

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Machine Learning-Based Approach for Tillage Identification Using Sentinel-1 Data

Published:2023-07-16 Issue: Volume: Page:
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Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Pandit Ankur¹,Bansal Pradhyumn²,Sawant Suryakant³,Mohite Jayantrao⁴,P Srinivasu⁵

Affiliation:

1. Tata Consultancy Services,R&I,Indore,Madhya Pradesh,India

2. Indian Institute of Technology,Kharagpur,India

3. Tata Consultancy Services,TRDDC,R&I,Pune,India

4. Tata Consultancy Services,R&I,Thane West,Maharashtra,India

5. Tata Consultancy Services,R&I,Hyderabad,TS,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282330.pdf?arnumber=10282330

Reference9 articles.

1. An empirical model and an inversion technique for radar scattering from bare soil surfaces

2. Assessment of soil surface roughness statistics for microwave remote sensing applications using a simple photogrammetric acquisition system

3. Quantitative Retrieval of Soil Moisture Content and Surface Roughness From Multipolarized Radar Observations of Bare Soil Surfaces

4. Density Estimates as Representations of Agricultural Fields for Remote Sensing-Based Monitoring of Tillage and Vegetation Cover

5. Artificial Neural Network Approach for Mapping Contrasting Tillage Practices

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