Crop type discrimination using Geo-Stat Endmember Extraction and machine learning algorithms

Prachi Singh; Prashant K Srivastava; Dharambhai Shah; Manish K Pandey; Akash Anand; Rajendra Prasad; Rucha Dave; Jochem Verrelst; Bimal K Bhattacharya; A S Raghubanshi

doi:10.1016/j.asr.2022.08.031

Crop type discrimination using Geo-Stat Endmember Extraction and machine learning algorithms

Adv Space Res. 2024 Jan;73(2):1331-1348. doi: 10.1016/j.asr.2022.08.031.

Authors

Affiliations

¹ Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India-221005.
² DST-Mahamana Centre of Excellence in Climate Change Research, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India-221005.
³ Electronics and Communication Engineering Department, Institute of Technology, NIRMA University, Ahmedabad, India-382481.
⁴ Department of Physics, IIT (BHU), Varanasi, India- 221005.
⁵ Department of Basic Science and Humanities, B.A. College of Agriculture, Anand Agricultural University, Anand, India-388110.
⁶ Image Processing Laboratory (IPL), University of Valencia, Valencia, Spain-46010.
⁷ Space Applications Centre, Indian Space Research Organisation, Ahmedabad, Gujarat, India-380015.

PMID: 38250579
PMCID: PMC7615539 (available on 2025-01-01)
DOI: 10.1016/j.asr.2022.08.031

Abstract

The identification of crop diversity in today's world is very crucial to ensure adaptation of the crop with changing climate for better productivity as well as food security. Towards this, Hyperspectral Remote Sensing (HRS) is an efficient technique based on imaging spectroscopy that offers the opportunity to discriminate crop types based on morphological as well as physiological features due to availability of contiguous spectral bands. The current work utilized the benefits of Airborne Visible Infrared Imaging spectrometer- New Generation (AVIRIS-NG) data and explored the techniques for classification and identification of crop types. The endmembers were identified using the Geo-Stat Endmember Extraction (GSEE) algorithm for pure pixels identification and to generate the spectral library of the different crop types. Spectral feature comparison was done among AVIRIS-NG, Analytical Spectral Device (ASD)-Spectroradiometer and Continuum Removed (CR) spectra. The best-fit spectra obtained with the Reference ASD-Spectroradiometer and Pure Pixel spectral library were then used for crop discrimination using the ten supervised classifiers namely Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Support Vector Machine (SVM), Minimum Distance Classifier (MDC), Binary Encoding, deep learning-based Convolution Neural Network (CNN) and different algorithms of Ensemble learning such as Tree Bag, AdaBoost (Adaptive Boosting), Discriminant and RUSBoost (Random Under Sampling). In total, nine crop types were identified, namely, wheat, maize, tobacco, sorghum, linseed, castor, pigeon pea, fennel and chickpea. The performance evaluation of the classifiers was made using various metrics like Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score. The classifier 2D-CNN was found to be the best with Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score values of 89.065 %, 0.871,87.565%, 89.541% and 88.678% respectively. The output of this work can be utilized for large scale mapping of crop types at the species level in a short interval of time of a large area with high accuracy.

Keywords: Continuum Removal; Crop Discrimination; Endmember Extraction; Hyperspectral; Supervised Classification.