Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms

Naveed Iqbal; Rafia Mumtaz; Uferah Shafi; Syed Mohammad Hassan Zaidi

doi:10.7717/peerj-cs.536

Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms

PeerJ Comput Sci. 2021 May 19:7:e536. doi: 10.7717/peerj-cs.536. eCollection 2021.

Authors

Naveed Iqbal^#¹, Rafia Mumtaz^#¹, Uferah Shafi¹, Syed Mohammad Hassan Zaidi¹

Affiliation

¹ National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science (SEECS), Islamabad, Pakistan.

^# Contributed equally.

Abstract

Crop classification in early phenological stages has been a difficult task due to spectrum similarity of different crops. For this purpose, low altitude platforms such as drones have great potential to provide high resolution optical imagery where Machine Learning (ML) applied to classify different types of crops. In this research work, crop classification is performed at different phenological stages using optical images which are obtained from drone. For this purpose, gray level co-occurrence matrix (GLCM) based features are extracted from underlying gray scale images collected by the drone. To classify the different types of crops, different ML algorithms including Random Forest (RF), Naive Bayes (NB), Neural Network (NN) and Support Vector Machine (SVM) are applied. The results showed that the ML algorithms performed much better on GLCM features as compared to gray scale images with a margin of 13.65% in overall accuracy.

Keywords: Classification; Feature extraction; GLCM; Machine learning; Remote sensing; Texture analysis; Unmanned aerial vehicles.

Grants and funding

The research work is funded by HEC under NRPU program, Pakistan. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.