The adaptive and automated analysis of hyperspectral data is mandatory in many areas of research such as physics, astronomy and geophysics, chemistry, bioinformatics, medicine, biochemistry, engineering, and others. Hyperspectra di er from other spectral data that a large frequency range is uniformly sampled. The resulting discretized spectra have a huge number of spectral bands and can be seen...

This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Particle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extraction. A formulation utilizing two swarms of particles was developed to optimize simultaneously a desired performance cri...

Title of Dissertation: HYPERSPECTRAL IMAGING AND PATTERN RECOGNITION TECHNOLOGIES FOR REAL TIME FRUIT SAFETY AND QUALITY INSPECTION Xuemei Cheng, Doctor of Philosophy, 2004 Dissertation directed by: Professor Yang Tao Department of Biological Resources Engineering Hyperspectral band selection and band combination has become a powerful tool and have gained enormous interest among researchers. An...

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...

Parametric geocoding is a standard procedure for orthorectification of hyperspectral airborne scanner data. Boresight parameters are estimated by using the image coordinates of precisely known ground reference points. To fully automate the processing of the recorded hyperspectral data ground reference points should be selected and matched by an algorithm without user interaction. In this articl...

Remote sensing has played a crucial role in mapping and understanding of the spatial pattern of mangrove forests and changes in its areal extent caused by natural disasters and anthropogenic forces. So far traditional pixel-based classification of multispectral imagery has been widely applied for broad mapping of mangrove covers. But the recent and more advanced hyperspectral data taken from se...

Hyperspectral imaging has emerged as a promising tool in the identification of objects and the state of objects, by their chemical and material composition. Hyperspectral imaging acquires spectral information at each pixel location across a wide range of the light spectrum. This enhanced spectrum information also comes with additional noise including spectral mixing, blurring and acquisition di...

The existence of characteristic structure, or shape, in complex data sets has been recognized as increasingly important for mathematical data analysis. This realization has motivated the development of new tools such as persistent homology for exploring topological invariants, or features, in data large sets. In this paper we apply persistent homology to the characterization of gas plumes in ti...

Real-time image processing requires high computational and I/O throughputs obtained by use of optoelectronic system solutions. A novel architecture that uses focal-plane optoelectronic-area I/O with a fine-grain, low-memory, single-instruction-multiple-data (SIMD) processor array is presented as an efficient computational solution for real-time hyperspectral image processing. The architecture i...

Dimensionality Reduction (DR) is a crucial first step in many hyperspectral processing algorithms. In some applications, such as target detection, change detection and classification, it is important to preserve the information associated to rare pixels, i.e. pixels scarcely represented in the data and containing spectral components that are linearly independent of the background. This paper pr...