Open AccessBook
Hyperspectral Data Processing: Algorithm Design and Analysis
TLDR
Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap.Abstract:
Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different categories. Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap.read more
Citations
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Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]
TL;DR: In this paper, a multispectral image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rock-like soil.
Proceedings ArticleDOI
Deep supervised learning for hyperspectral data classification through convolutional neural networks
TL;DR: This work proposes a deep learning based classification method that hierarchically constructs high-level features in an automated way and exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task.
Journal ArticleDOI
Hyperspectral image reconstruction by deep convolutional neural network for classification
TL;DR: Experimental results indicate that framework built based on CNN and ELM provides competitive performance with small number of training samples, and the average accuracy of ELM can be improved as high as 30.04%, while performs tens to hundreds of times faster than those state-of-the-art classifiers.
High Performance Computing
TL;DR: The key elements of the Core Program will be described including the construction of a UK e-Science Grid and the need to develop a data architecture for the Grid that will allow federated access to relational databases as well as flat files.
Journal ArticleDOI
A review on spectral processing methods for geological remote sensing
TL;DR: A novel categorization scheme is proposed that groups the techniques into knowledge-based and data-driven approaches, according to the type and availability of reference data, to yield some of the most robust processing techniques available to multi- and hyperspectral remote sensing.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Related Papers (5)
Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach
J.C. Harsanyi,Chein-I Chang +1 more
Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution
Irving S. Reed,Xiaoli Yu +1 more