of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy HYPERSPECTRAL ENDMEMBER DETECTION AND BAND SELECTION USING BAYESIAN METHODS By Alina Zare December 2008 Chair: Paul Gader Major: Computer Engineering Four methods of endmember detection and spectral unmixing are described. The methods de...

Hyperspectral images, contain data from a large number of contiguous bands and, therefore, cannot be displayed directly using a color display system. In this paper, an independent component analysis-based (ICA-based) approach for the problem of fusing hyperspectral images to three-band images for color display purposes is proposed. Correlation coefficient and mutual information (ICA-CCMI) are u...

Hyperspectral data provide new capabilities for discriminating spectrally similar classes, but such class signatures sometimes will be difficult to analyze. To incorporate reliable useful information could help, but at the same time, may also lead increased dimensionality of the feature vector making the hyperspectral data larger than expected. It is challenging to apply discriminative informat...

This paper describes the design and performance of an image capture simulator. The general model underlying the simulator assumes that the image capture device contains multiple classes of sensors with different spectral sensitivities and that each sensor responds in a known way to irradiance over most of its operating range. The input to the simulator is a set of narrow-band images of the scen...

A novel kernel framework for hyperspectral image classification based on relevance vector machine (RVM) is presented in this paper. The new feature extraction algorithm based on Mexican hat wavelet kernel non-negative matrix factorization (WKNMF) for hyperspectral remote sensing images is proposed. By using the feature of multi-resolution analysis, the new method of nonlinear mapping capability...

Since the traditional Support Vector Machine (SVM) algorithm has high classification accuracy at the expense of huge training samples, an automatic hyperspectral image classification method based on subspace partition and SVM (ASP-SVM) algorithm is proposed. In this proposed method, the endmembers were extracted as the representative samples for each class, and a coarse classification result is...

One of the objectives of precision agriculture is rapid location of fields information to reduce the investment and improve the environment. Based on near infrared (NIR) hyperspectral images, this paper outlines an approach using hyperspectral imaging technology, combining spectral analysis methods and supervised classification methods for the extraction and analysis of farmland objects. This p...