Hyperspectral Remote Sensing for Land Cover Classification and Chlorophyll Content Estimation using Advanced Machine Learning Techniques

Abstract

In the recent years, remote sensing data or images have great potential for continuous spatial and temporal monitoring of Earth surface features. In case of optical remote sensing, hyperspectral (HS) data contains abundant spectral information and these information are advantageous for various applications. However, high-dimensional HS data handling is a very challenging task. Different techniques are proposed as a part of this thesis to handle the HS data in a computationally efficient manner and to achieve better performance for land cover classification and chlorophyll content prediction. Prior to start the HS data application, multispectral (MS) data are also analyzed in this thesis for crop classification. Multi-temporal MS data is used for crop classification. Landsat-8 operational land imager (OLI) sensor data are considered as MS data in this work. Surface reflectances and derived normalized difference indices (NDIs) of multi-temporal MS bands are combinedly used for the crop classification. Different dimensionality reduction techniques, viz. feature selection (FS) (e.g. random forest (RF) and partial informational correlation (PIC) measure-based), linear (e.g. principal component analysis (PCA) and independent component analysis) and nonlinear feature extraction (FE) (e.g. kernel PCA and Autoencoder), to be employed on the multi-temporal surface reflectances and NDIs datasets, are evaluated to detect the most favorable features. Subsequently, the detected features are used in a promising nonparametric classifier, support vector machine (SVM), for crop classification. It is found that all the evaluated FE techniques, employed on the multi-temporal datasets, resulted in better performance compared to FS-based approaches. PCA, being a simple and efficient FE algorithm, is well-suited in crop classification in terms of computational complexity and classification performances. Multi-temporal images are proved to be more advantageous compared to the single-date imagery for crop identification. HS data...