Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems. This study focuses on the challenges facing large-scale species mapping using a fusion of Light Detection and Ranging (LiDAR) and hyperspectral imagery. Here we build upon previous work on airborne s...

Title of Thesis: Detection of Fecal Contamination on Cantaloupes and Strawberries Using Hyperspectral Fluorescence Imagery Angela M. Vargas, Master of Science, 2006 Thesis directed by: Professor, Yang Tao Biological Resources Engineering Department Fluorescence methods are widely used for investigation of biological materials, and in recent years have also been used to monitor food quality and ...

We consider the challenge of detection of chemical plumes in hyperspectral image data. Segmentation of gas is difficult due to the diffusive nature of the cloud. The use of hyperspectral imagery provides non-visual data for this problem, allowing for the utilization of a richer array of sensing information. We consider several videos of different gases taken with the same background scene. We i...

Utilization of remote sensing multiand hyperspectral imagery has shown a rapid increase in many areas of economic and scienti c signi cance over the past ten years. Hyperspectral sensors, in particular, are capable of capturing the detailed spectral signatures that uniquely characterize a great number of diverse surface materials. Interpretation of these very high-dimensional signatures, howeve...

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...

Dimensionality reduction is a major task in remote sensing images. Feature selection is applied for performing dimensionality reduction. It selects the spectral features(i.e. Bands) and find a feature subset that preserves the semantics of the hyperspectral image. Based on particle swarm optimization (PSO), this paper proposes multi-objective functions for selecting the spectral feature subsets...

Two improved local-region filters, adaptive weighted filter (AWF) and collaborative representation filter (CoRF), are proposed for feature extraction and classification in hyperspectral imagery. The local-region filters generate spatial-spectral features of a hyperspectral pixel by incorporating its surrounding pixels. The work of this paper is an extension of our previously introduced local av...

A combined geometric and radiometric processing chain for hyperspectral data is presented. This paper describes the ortho-rectification solution for the geometric part of the whole problem. A parametric geocoding approach (PARGE) has been chosen. The geometrical model strictly considers all navigational parameters engaging a forward transformation methodology. For the implementation, a number o...

In this work, a novel methodology based on hyperspectral imagery with enhanced Darkfield microscopy for probing and characterizing changes in blood cell components was tested. Two main categories of blood cells were analyzed, red and white blood cells. Unique spectral signatures of ordinary and most common deformed morphologies of red blood cells were identified. Moreover, examination of white ...

A hyperspectral image can be considered as an image cube where the third dimension is the spectral domain represented by hundreds of spectral wavelengths. A hyperspectral image pixel is actually a column vector with dimension equal to the number of spectral bands and contains valuable spectral information that can be used to detect and identify a variety of nature and man-made material. Some sp...