Hyperspectral imaging is an emerging technology in the field of biomedical engineering which may be used as a non-invasive modality for cancer characterization. In this project, we propose to investigate near-infrared (NIR) hyperspectral imaging for the characterization of canine mammary cancer. Nearinfrared hyperspectral imaging has been used for the detection of various kinds of human cancer;...

This paper presents genetic algorithm based band selection and classification on hyperspectral image data set. Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. In this paper, first filtering based on 2-D Emp...

Hyperspectral imaging technique was employed to determine spatial distributions of chlorophyll (Chl), and carotenoid (Car) contents in cucumber leaves in response to angular leaf spot (ALS). Altogether, 196 hyperspectral images of cucumber leaves with five infection severities of ALS were captured by a hyperspectral imaging system in the range of 380-1,030 nm covering 512 wavebands. Mean spectr...

30 Abstract—The blessings of hyperspectral remote sensing are manifold and it has enabled researchers to locate, map and identify different materials on the surface of Earth. Hyperspectral remote sensing play a key role in mineral mapping activities and it can be a much powerful and cost effective tool for mineral development activities in a developing country like Pakistan where there are rich...

Presented at the Book and Paper Group session, AIC 37th Annual Meeting, May 20–23, 2009, Los Angeles, California. tested by exposure to heat and humidity, highintensity light, and elevated humidity at room temperature. Changes were evaluated against unaged or untreated controls. Methods of evaluation include hyperspectral imaging, color measurement, pH, iron (II) testing using bathophenthroline...

Earth science research and application requirements for multispectral data have often been driven by currently available remote sensing technology. Few parametric studies exist that specify data required for certain applications. Consequently, data requirements are often defined based on the best data available or on what has worked successfully in the past. Since properties such as spatial res...

An unsupervised neuro-fuzzy system, Gaussian fuzzy self-organizing map (GFSOM), is proposed for hyperspectral image classification. This algorithm operates by integrating an unsupervised neural network with a Gaussian function-based fuzzy system. We also explore the potential for hyperspectral image analysis of three other artificial intelligence (AI)-based unsupervised techniques popular for m...

Hyperspectral imagery has proven its potential in many research applications, especially in the field of environmental sciences. Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms, but mini-UAV (Unmanned Aerial Vehicle) platforms (<20 kg) are now emerging. On such platforms, payload restrictions are critical, so sensors must be selected according to strin...

Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method from the following two a...

A Gibbs sampler for piece-wise convex hyperspectral unmixing and endmember detection is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data is often nonconvex. Furthermore, in standard unmixing methods, endmembers are generally represented as a single point in the high dimension...