Advanced Spectral Spatial Approaches for Dimensionality Reduction of Hyperspectral Data

Abstract

Recent advances in sensor technology have enabled the collection of large data in hyperspectral remote sensing. Although rich spectral information is captured in hundreds of narrow contiguous bands, the hyperspectral data possess several limitations such as mixed pixels, high intraclass variability, interclass similarity, and the curse of dimensionality which restricts the potential of conventional machine learning classifiers. Dimensionality reduction (DR) and incorporation of spatial information can be taken into account to increase the interpretability of hyperspectral data. The thesis mainly focuses on the implementation of different approaches for DR of hyperspectral data to address the curse of dimensionality, limited samples and labelled data issues inherent in hyperspectral data. First, a quality measure based on the co-ranking matrix has been proposed for the performance evaluation of 15 DR techniques for mineral exploration. The selection of appropriate techniques for a particular task is challenging due to the diversity and ever-increasing number of DR techniques. A few important aspects in this regard have been explored in detail. Clustering is performed using the K-means algorithm and the relationship between the quality index and clustering accuracy has been examined concurrently for the first time in hyperspectral remote sensing. Furthermore, the loss of quality in the process of DR has also been analyzed which provides sufficient input for the end-user to select an appropriate DR technique. Second, the ability of the Convolutional Neural Network (CNN) for supervised learning of hyperspectral data is explored. A fast and compact hybrid CNN which combines the strengths of 3D and 2D convolutions to extract joint spectral-spatial information has been proposed to analyze the impact of different feature extraction techniques on classification performance. The effect of input patch size on final results has been well demonstrated. A detailed investigation of classification accuracy, execution time, and comparison with nine state-of-the-art approaches has been demonstrated. ii Next, a novel deep feature selection strategy using autoencoders inspired by knowledge distillation has been implemented for the model compression and selection of informative bands. The potential of convolutional autoencoders has been well explored in selecting discriminative bands. Sensitivity analysis tests and different applications have been considered to verify the generalization capability of the proposed model. The potential of unsupervised learning schemes has been discussed in detail. Finally, a generator model based on Generative Adversarial Networks (GAN) has been proposed for virtual sample generation and compact representation of hyperspectral data. The training instability issue in Vanilla GAN has been addressed by the effective implementation of deep convolutional GANs. By comparing the spectra of the generated hyperspectral images to the corresponding real ones, the quality of the images is assessed. The potential of augmented data for improvement in classification accuracy has also been investigated.