In this paper, the problem of compressing hyperspectral images using a classification point of view is examined. The goal is to compress with loss an image in order to obtain interesting compression ratio with the constraint to be near lossless for a classification algorithm. Our proposed method is based on a spectral vector quantization performed by the Kohonen’s Self Organizing Map and an ent...

We describe a fast method for dimensionality reduction and feature selection of ratio features for classification in hyperspectral data. The case study chosen is to discriminate internally damaged almond nuts from normal ones. For this case study, we find that using the ratios of the responses in several wavebands provides better features than a subset of waveband responses. We find that use of...

Spectral imagery from the HYDICE instrument was analyzed for the purpose of target detection and identification. Data in the 0.4 2.5 micron wavelength range were acquired during the FOREST RADIANCE data collect. Data were analyzed from an area consisting of grassy fields and forest areas. A variety of targets were deployed in the field, with ground truth spectral measurements made. Analysis of ...

Reed beds are the second largest producer of biomass in Olkiluoto Island. Quantitative information on the extent and amount of reed stands is an integral part of the biosphere assessment related to long-term safety analysis of nuclear fuel repository site currently under construction. The major challenge in reed bed mapping is discrimination between reed and other green vegetation. Spectral fie...

The aim of this study is to validate the Spectral Angle Mapper (SAM) algorithm for geological mapping in Central Jebilet Morocco and compare the results between high and medium spatial resolution sensors, such as Quickbird and Landsat TM respectively. The geology of the study area is dominated by the Sahrlef schist, which is intruded by numerous bodies of acidic and basic magmatic rocks and len...

Image fusion is a useful tool in integrating a high-resolution panchromaticimage (HRPI) with a low-resolution multispectral image (LRMI) to produce a highresolutionmultispectral image (HRMI). To date, many image fusion techniques have beendeveloped to try to improve the spatial resolution of the LRMI to that of the HRPI with itsspectral property reliably preserved. However, many studies have in...

A hyperspectral image can be considered as an image cube where the third dimension is the spectral domain represented by hundreds of spectral wavelengths. As a result, 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 account for pixel variability, similarity, and discrimina...

Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. New algorithms have been developed for hyperspectral data classification lately, but also traditional classification algorithms have often been used. This study compares different classification algorithms for classification of vegetation using imaging spectrometer da...

This paper presents a statistical method for deriving optimal spatial sampling schemes. It focuses on ground verification of minerals derived from hyperspectral data. Spectral angle mapper (SAM) and spectral feature fitting (SFF) classification techniques were applied to obtain rule mineral images. The rule images provide weights that are utilized in simulated annealing. A HyMAP 126channel airb...