Evaluation of Feature Selection and Feature Extraction Techniques on Multi-Temporal Landsat-8 Images for Crop Classification

Abstract

Recent advancements in the remotely sensed data products and machine learning algorithms are utilized effectively for classification of crops over a considerable large area. This article proposes the use of feature extraction techniques to be employed on the multi-temporal Landsat-8 OLI sensor’s surface reflectances and derived Normalized Difference Indices datasets to classify different crop types. Numerous dimension reduction techniques, viz., feature selection (random forest and PIC measure based), linear (principal component analysis (PCA) and independent component analysis) and nonlinear feature extraction (kernel PCA and Autoencoder), are evaluated to detect most favourable features which should be apt for classification of crops. Subsequently, the detected features are used in a promising nonparametric classifier, support vector machine, for crop classification. It has been found that all the evaluated feature extraction techniques, employed on the multi-temporal datasets, result in better performance compared to feature selection-based approaches. PCA, being a simple and efficient feature extraction algorithm, is well-suited in this classification study and extracted features can classify the crops with an average overall accuracy of 94.32%. Most of the crop types achieve user and producer accuracy of more than 90%. Multi-temporal images prove to be more advantageous compared to the single-date imagery for crop identification.