We describe the development of a real-time processing tool for hyperspectral imagery based on off-the-shelf equipment and higher level programming language implementation (C++ and Java). The algorithms we developed are derived from previously introduced spectra matching and feature extraction tools. The first group is based on spectra identification and spectral screening, a method that allows ...

Anomaly detection is one of the most popular applications in hyperspectral remote sensing image analysis. Anomaly detection technique does not require any prior features or information of targets of interest and has draw the increasing interest in target detection domain for hyperspectral imagery (HSI) in the recent twenty years. From hyperspectral data, the approximately continuous spectral fe...

efforts to improve the quality of bakery products are increasing every day. several techniques were developed for this purpose. in this study the possibility of betterment and optimization of quality properties of dough and barbari bread with emphasis on process variables using rotatable response surface methodology were investigated. the independent process variables for baking process were do...

This paper is concerned with the comparison of three types of Gaussian based Artificial Neural Networks in the very high dimensionality classification problems found in hyperspectral signal processing. In particular, they have been compared for the spectral unmixing problem given the fact that the requirements for this type of classification are very different from other realms in two aspects: ...

In this paper, a novel anomaly detection algorithm is proposed for hyperspectral imagery, which is the extended RX algorithm based on spectral dimension transformation and spatial filter (STSF-RX). Firstly, minimum noise fraction (MNF) transform is performed on the original hyperspectral images, by setting a SNR threshold, and obtains MNF transform matrices that the SNR of their corresponding b...

Hyperspectral imagery has emerged as a popular sensing modality for a variety of applications, and sparsity based methods were shown to be very effective to deal with challenges coming from high dimensionality in most hyperspectral classification problems. In this work, we challenge the conventional approach to hyperspectral classification, that typically builds sparsity-based classifiers direc...

Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we pres...

Hyperspectral imaging is increasingly used in various environmental measurement applications. Airborne hyperspectral sensors enables ground sampling distances (GSD) below 1 meter with high spectral resolution. In order to utilize this data in quantitative remote sensing applications, an accurate radiometric correction of the imagery has to be performed. In this article we present a radiometric ...