Improved early-stage crop classification using a novel fusion-based machine learning approach with Sentinel-2A and Landsat 8-9 data.

Journal: Environmental monitoring and assessment
Published Date:

Abstract

Crop classification during the early stages is challenging because of the striking similarity in spectral and texture features among various crops. To improve classification accuracy, this study proposes a novel fusion-based deep learning approach. The approach integrates textural and spectral features from a fused dataset generated by merging Landsat 8-9 and Sentinel-2A data using the Gram-Schmidt fusion approach. The textural features were extracted using the multi-patch Gray Level Co-occurrence Matrix (GLCM) technique. The spectral features, namely the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), were obtained using the spectral index method. The five machine learning methods (deep neural network, 1D convolutional neural network, decision tree, support vector machine, and random forest) were trained using textural and spectral parameters to develop classifiers. The proposed approach achieves promising results using deep neural network (DNN), with an accuracy of 0.89, precision of 0.88, recall of 0.91, and F1-score of 0.90. These results demonstrate the effectiveness of the fusion-based deep learning approach in enhancing classification accuracy for early-stage crops.

Authors

  • Muhammad Daniyal Jamil
    Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Vehari, 61100, Pakistan.
  • Muhammad Zahid Abbas
    Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Vehari, 61100, Pakistan.
  • Muhammad Farhan Saeed
    Department of Environmental Sciences, COMSATS University Islamabad, Vehari Campus, Vehari, 61100, Pakistan.
  • Aftab Jamal
    Department of Soil and Environmental Sciences, Faculty of Crop Production Sciences, The University of Agriculture, Peshawar, 25130, Pakistan.
  • Muhammad Mubeen
    Department of Biotechnology, COMSATS University Islamabad, Vehari Campus, 61100, Vehari, Pakistan.
  • Ali Zakir
    Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo 182-8585, Japan.
  • Iftikhar Ahmad
    Department of Environmental Sciences, COMSATS University Islamabad, Vehari-Campus, Vehari, 61100, Pakistan. Electronic address: iftikharahmad@ciitvehari.edu.pk.
  • Rimsha Jameel
    Department of Environmental Sciences, COMSATS University Islamabad, Vehari Campus, Vehari, 61100, Pakistan.
  • Katarzyna Pentoś
    Institute of Agricultural Engineering, Wroclaw University of Environmental and Life Sciences, 37B Chełmonskiego Street, 51-630, Wrocław, Poland.
  • Yaser Hassan Dewir
    Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, 11451, Riyadh, Saudi Arabia.
  • Jakub Černý
    Department of Silviculture, Faculty of Forestry and Wood Technology, Mendel University, Zemědělská 3, 613 00, Brno, Czech Republic. jakub.cerny@mendelu.cz.