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A Machine Learning and Deep Learning Approach for Accurate Crop-Type Mapping Using Sentinel-1 Satellite Data

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Inventive Communication and Computational Technologies (ICICCT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 757))

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Abstract

Crop classification offers relevant data for crop management, ensuring food safety, and developing agricultural policies. Mapping the crops with high resolution has great significance in determining the position of the crop and effective agricultural monitoring. However, high data costs and poor temporal resolution of satellite data make it difficult to detect the different crops in the field. Therefore, the goal of this survey is to provide an effective analysis of various land cover maps in agriculture using a time series of C-band Sentinel-1 synthetic aperture radar (SAR) data. The various methods based on vertical transmit-vertical receive (VV) and vertical transmit—horizontal receive (VH) polarizations are analyzed to produce better accuracy value for different types of agricultural land. This survey analyzed the different types of existing classification methods such as machine learning and deep learning algorithm used in Sentinel-1 satellite data. The overall accuracy (OA), kappa coefficient, user accuracy (UA), producer accuracy (PA), and F1-score are considered key parameters for defining the effectiveness of crop-type classification in land cover types. This comprehensive research supports the researchers to obtain the best solutions for the current issues in crop-type mapping using Sentinel-1 SAR data.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering (UCOE), Punjabi University, Patiala, India

    Sanjay Madaan

  2. Department of Computer Science, Punjabi University, Patiala, India

    Sukhjeet Kaur

Authors
  1. Sanjay Madaan
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  2. Sukhjeet Kaur
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Corresponding author

Correspondence to Sanjay Madaan .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India

    G. Ranganathan

  2. Department of Computer Science (HUMAIN-Lab), International Hellenic University, Thessaloniki, Greece

    George A. Papakostas

  3. Information Systems and Operations Management (ISEG), University of Lisbon, Lisboa, Portugal

    Álvaro Rocha

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Madaan, S., Kaur, S. (2023). A Machine Learning and Deep Learning Approach for Accurate Crop-Type Mapping Using Sentinel-1 Satellite Data. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_41

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