Advanced Spectral Spatial Processing Techniques For Hyperspectral Image Analysis
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Advanced Spectral-spatial Processing Techniques for Hyperspectral Image Analysis
The main objective of this research is to design and implement novel spectral-spatial processing techniques for hyperspectral image analysis and applications. Although the high dimensionality of hyperspectral image data makes its transmission and storage difficult, the uncompressed data format is still preferred as it avoids compression loss which may degrade classification accuracy. In this thesis, a quality-assured lossy compression scheme based on a modified three dimensional discrete cosine transform is proposed. This novel technique is demonstrated to maintain the integrity of hyperspectral data without degrading the classification accuracy. Furthermore, this work has led to the creation of an effective spectral feature extraction technique which uses curvelet transform and singular spectrum analysis. In addition to this, an original classification framework which combines joint bilateral filtering and an improved sparse representation classifier is presented. Experimental results show that the proposed methodologies outperform most of the state-of-the-art feature extraction and classification techniques commonly employed in the hyperspectral community. This work also demonstrates that hyperspectral imaging combined with advanced signal processing is an effective technology for food quality control applications. For example, when applied to the challenge of performing hyperspectral imaging-based meat quality assessment, the techniques proposed in this work are shown to provide a more effective solution than conventional visible and near-infrared spectroscopic technology. Finally, this thesis provides the first set of results of assessing the quality of beef and lamb samples using an improved data regression technique. To sum up, the outcome of this thesis advances the hyperspectral imaging community by proposing several novel methodologies, and extensive experiments have been conducted to demonstrate their superiority.
Hyperspectral Image Analysis
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Deep Learning for Hyperspectral Image Analysis and Classification
This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.