Deep learning method for crop classification using remote sensing data

Abstract

The Red Edge Position (REP) index plays an important role in agricultural remote sensing applications. A wavelength from 350 nm to 990 nm is the common range for green. In this chapter, we have focused mainly on crop classification using the deep learning method. We have presented a study of crop classification using deep learning methods on hyperspectral remote sensing data. Deep learning is the evolved form of artificial neural network (ANN). It is based on a biological concept that deals with the network of neurons in a brain. To solve problems regarding crop classification, many machine-learning methods are used by researchers. Traditional machine-learning algorithms, including support vector machine, decision tree-based, and random forest, work on structured data only. Remote sensing data is unstructured data. Hence more computational overheads are needed to organize the unstructured data into structured ones. One of the most adaptable state-of-the-art approaches for feature extraction and classification of unstructured and structured data is deep learning. Thus we have focused on deep learning convolutional neural network (CNN) for feature extraction and classification of crops.