A sample selection method based on similarity measure and fuzziness for crop classification from hyperspectral data

Abstract

Crop mapping using remote sensing data is essential for predicting crop yield and future requirements. However, it can be accomplished by having full ground truth data corresponding to the samples captured by remote sensors. In a practical scenario, generally, we have limited ground-truth data where available crop samples are less than the number of spectral bands of a hyperspectral image. This paper proposes a sample selection method to select samples from a hyperspectral image based on similarity measure and fuzziness in the samples. We use a single-ground truth pixel of each crop for crop-sample selection. Firstly, we select the number of samples for each crop class using Spectral Information Diversion and tangent of Spectral Angle Mapper (SStan) measure from a selected region of the hyperspectral image. Then, we compute fuzziness for each selected crop sample to generate a set of final samples. We have evaluated the proposed method on an Airborne Visible near InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) dataset and two benchmark datasets, i.e., Indian Pines and Salinas. Finally, classification results using three supervised classifiers, namely, support vector machine, k-nearest neighbourhood and random forest, have been shown in terms of overall accuracy and kappa coefficient. It can be demonstrated that the proposed method outperforms some prevailing methods by at least 4.5% for 5 samples and 11% for 10 samples for the Indian Pines dataset.