Rock type classification using MGM peak-fitting analysis of hyperspectral reflectance spectra (0.35-2.50 um), Sudbury igneous complex, Ontario

by Susan M. J. Cook

Publisher: Laurentian University, School of Graduate Studies in Sudbury, Ont

Written in English
Published: Pages: 297 Downloads: 868
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Edition Notes

Statementby Susan M.J. Cook.
SeriesCanadian theses = Thèses canadiennes
The Physical Object
Paginationxvi, 297 l. ;
Number of Pages297
ID Numbers
Open LibraryOL22249673M
ISBN 109780494201664

Adams, J.B., , Visible and near IR diffuse reflectance spectra of pyroxenes as applied to remote sensing of solid objects in the solar system. Journal of Geophysical Research, 79, pp. Adams, J.B., and Smith, M.O., , Spectral mixture modelling: A new analysis of rock and soil types at the Viking Lander I site. a particular type of material, and the tip of the spike represents the purest spectral representation of the material. These extrema are typically effective as "endmembers" for use with hyperspectral classification methods. Both the eigenvalues and the MNF images (eigenimages) are used to evaluate the dimensionality of the data.   The purpose of the program is to produce the results fast, easily and in a convenient way for the user (see Outputs). Initially, its purpose was to perform index analysis in hyperspectral and multispectral satellite imagery. It has been used and tested in fused hyperspectral products for quality assessment of the spectral fidelity. Compressive sensing (CS) is an enabling technology for reducing the overall data processing and SWaP requirements. This paper explores the viability of performing classification for hyperspectral data on a compressively sensed band domain (CSBD) via CS instead of the original data space, without performing sparse reconstruction.

Fig. 2 shows laboratory reflectance spectra from selected samples from Perry Park and Canon City, and summarizes the spectral variability that we encountered among all the expansive clays and shales collected. Spectrum 1 (PP) is from a bentonite bed, composed of pure smectite in clay fraction (total mineralogy: 98% smectite, 2% non-clay material), with the highest swell potential possible. ADVANCES IN HYPERSPECTRAL IMAGE CLASSIFICATION [EARTH MONITORING WITH STATISTICAL LEARNING METHODS] Despite all these commonalities, the analysis of hyperspectral images turns out to be more difficult, especially because of the high dimensionality of the pixels, the particular noise and uncertainty sources observed, the high spatial and. The spectral range of the instrument is to nm at nm intervals. High-resolution hyperspectral images of highwalls, outcrops, hand samples and drill core were collected in the field. Spectra from the images closely matched those of specific minerals present and were used to classify the images. The different methods were compared using hyperspectral data acquired from ore-bearing rocks under different environmental conditions. The calibration of class probabilities improved the overall performance for almost all algorithms tested; an improvement of over 10% was observed in some cases.

  T1 - Evaluation of rock faces with hyperspectral imaging. AU - Combs, John H. AU - Kudenov, Michael W. AU - Craven, Julia. AU - Kemeny, John M. PY - /12/6. Y1 - /12/6. N2 - Hyperspectral imaging is a technology that uses non-visible portions of the electromagnetic spectrum to identify and categorize different objects. Landgrebe: Hyperspectral Data Analysis SPIE Bios99, San Jose CA, Jan. 29, 2 These factors placed the focus upon the use of spectral variations, i.e., using the spectral distribution of energy emanating from a pixel to label the contents of that pixel. Then, with the use of pattern recognition methods, a thematic map of a region.   Hyperspectral image analysis. A tutorial. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the. Hyperspectral Imaging •The Method Hyperspectral imaging is based on light reflectance from an exposed surface (long and narrow like a ‘line’) of concrete or steel members. As a hyperspectral camera moves in a direction perpendicular to the ‘line’, the entire surface of a structural member can be scanned.

Rock type classification using MGM peak-fitting analysis of hyperspectral reflectance spectra (0.35-2.50 um), Sudbury igneous complex, Ontario by Susan M. J. Cook Download PDF EPUB FB2

Spectral reflectance data for the deposit were published by Rowan and others (5). The data consist of 47 bands from to nm in nm intervals each collected at four polarizations. Data preprocessing and most of the subsequent analysis was done using basic procedures from ENVI software (Research Systems, Inc., Boulder, CO).

Abstract. Hyperspectral imaging (HI) is a method of observing and enhancing geological rock properties that are not readily apparent visually. Originally developed for the mining industry, HI uses a combination of short-wave infrared light (SWIR) and long-wave infrared light (LWIR) to create a visual ‘map’ of the minerals on the surface of a core that respond to reflectance principles.

Hyperspectral Image Classification Based on Logical Analysis of Data Abstract: Hyperspectral imaging is a relatively new technique for remote sensing.

Earth observation technology and applications are migrating from just plane imaging in Author: Ayman Mahmoud Ahmed, Sara K. Ibrahim, Soumaya Yacout. A Portable Infrared Mineral Analyzer II (PIMA II) field spectrometer was used to measure infrared reflectance spectra (15 μm) of split drill core at 1 cm intervals Rock type classification using MGM peak-fitting analysis of hyperspectral reflectance spectra book both the along-core and cross-core directions.

These data were formatted into an image cube similar to that acquired by an imaging spectrometer with spectral channels, and multi-spectral and hyperspectral analysis techniques were used for by: Hyperspectral image cube showing relation to spectra.

Top image is a false color composite of rocks and vegetation with red = nm, green = nm, and blue = nm. A radiant energy value is recorded for each data point (pixel) in the image for every wavelength sampled so that a spectrum is collected for each pixel in the Size: 1MB.

use Vertex Component Analysis (VCA) to reduce the dimen­ sion ofhyperspectral data [7]. We suppose that the spectrum ofeach pixel is a linear mixture ofthe spectra of different chemical species (refered as endmembers).

This linear mix­ ture model is physically valid for the reflectance ofthe surface ofthe Earth without being affected by the. Hyperspectral images are images captured in hundrends of bands of the electromagnetic spectrum. This project is focused at the development of Deep Neural Network for landcover classification in hyperspectral images.

Land-cover classification is the task of assigning to every pixel, a class label that represents the type of land-cover. Hyperspectral sensors record the reflectance from the Earth's surface over the full range of solar wavelengths with high spectral resolution.

The resulting high-dimensional data contain rich. Abstract: Hyperspectral image classification, an astonishing tool to distinguish the land covers in remote sensed hyperspectral images, has been investigated by multiple disciplines such as geoscience, environmental science, mathematics, and computer vision.

Following early machine learning (e.g., support vector machines and neural networks) and feature extraction theories (e.g., principal. Application of Hyperspectral Image Analysis Hyperspectral imagery has been used to detect and map a wide variety of materials having characteristic reflectance spectra.

For example, hyperspectral images have been used by geologists for mineral mapping (Clark et al.,) and to detect soil.

Wavelet analysis, which applies wavelet transforms to reflectance spectra, and broadband modelling, which estimates bitumen content directly from resampled hyperspectral reflectance data, were.

The prototype Hyperspectral Analysis process in TNTmips V provides the specialized, interactive analysis tools that are required to fully exploit the spectral range and spectral resolution provided by hyperspectral datasets. The process allows you to work with two different types of objects: hyperspectral images and spectral libraries.

Cui, S. Prasad, “Multi-Scale Sparse Representation Classification for Robust Hyperspectral Image Analysis,” in proceedings of the 1’st IEEE Global Conference on Signal and Image Processing (GlobalSIP) on New Sensing and Inference Methods, pp.


Bruce, Associate Professor. hyperspectral imaging in geology The foremost airborne application of hyperspectral imagery provides mineral mapping for exploration clients in the mining, oil, gas and geothermal sectors, over large and often remote and inaccessible areas.

Grapevine varietal classification is an important plant phenotyping issue for grape growing and wine industry.

This task has been achieved from destructive techniques like classic ampelography and DNA analysis under laboratory conditions. This work displays a new approach for the classification of a high number of grapevine (Vitis vinifera L.) varieties under field conditions using on-the-go.

Abstract: Hyperspectral sensors record the reflectance from the Earth's surface over the full range of solar wavelengths with high spectral resolution. The resulting high-dimensional data contain rich information for a wide range of applications. However, for a specific application, not all the measurements are important and useful.

Abstract: Hyper spectral image processing is becoming an active topic in remote sensing and other applications in current times. Hyper spectral images can easily distinguish materials which are spectrally similar. Many techniques are available to classify hyper spectral images which are mainly deals with the curse of dimensionality and working with few training data issues which confront.

The hyperspectral images in the spectral range of ~1 nm of black bean were acquired using the developed hyperspectral imaging system, and the reflectance.

measure infrared reflectance spectra ( - µm) of split drill core at 1 cm intervals in both the along-core and cross-core directions. These data were formatted into an image cube similar to that acquired by an imaging spectrometer with spectral channels and multispectral and hyperspectral analysis techniques were used for analysis.

Color. Examples on the use of spectra–structure correlations are given. on the thermal maturity of the dispersed organic matter in the host rock. Three sample types were analyzed: fossilized. around 4 cm², whereas the spatial pixel size of HySPEX spectra.

depends on the object distance and the foreoptic. s (5x5 pixel window with 1 m lenses alike. 3: Laboratory Set Up of the HySpex System. Hyperspectral Data Analysis. The pre-processing of the data was performed using an in. A new framework for hyperspectral image classification using multiple spectral and spatial features - Duration: MIT Education 2, views.

Identification and analysis of rock and minerals by LLA Instruments GmbH hyperspectral imaging NIR camera uniSPECHSI.

Real-time detection of the material stream and online visualisation by in. In this article, we concentrated on the use of hyperspectral reflectance measurements for applications in historical document analysis. However, it is known that important information about documents can also be obtained from ultra-violet fluorescence and near-infrared luminescence measurements.

Abstract. The detailed spectra defined in a hyperspectral images posses new image processing challenges and exciting opportunities. Unlike its multispectral counterpart, hyperspectral imagery captures a level of spectral resolution that contains unique compositional and structural information about the landscape not available in other forms of remotely sensed imagery.

Methodology for hyperspectral image classification using novel neural network Suresh Subramaniana, Nahum Gata, Michael Sheffield a, Jacob Barhenb, Nikzad Toomarianc aOpto-Knowledge Systems Inc. (OKSI), Spencer Street, SuiteTorrance, CA bOak Ridge National Laboratory, Bethel Road, Bldg.

/, Oak Ridge, TN cJet Propulsion Laboratory, Oak Grove. Method 1: Information Entropy - This method is based on evaluating each band separately using the information entropy measure based on the the probability density function of reflectance values in a hyperspectral band and the number of distinct reflectance values.

The probabilities are estimated by computing a histogram of reflectance values. Spectral Libraries / Reflectance Spectra The next task is to identify the minerals whose reflectance spectra most nearly resemble the image spectra. ENVI includes several spectral libraries. For the purposes of this exercise, you will use the JPL Spectral Library (Groves et al., ) and the USGS Spectral Library (Clarke et al., ).

of red light than soils. Several libraries of reflectance spectra of natural and man-made materials are available for public use. These libraries provide a source of reference spectra that can aid the interpretation of hyperspectral and multispectral images.

ASTER Spectral Library, USGS Spectral Library are some of such spectral libraries. The Hyperspectral Analysis process in TNTmips provides the specialized tools you need to fully exploit the spectral range and spectral resolution of your hyperspectral images.

On-the-fly reflectance calibration and an integrated spectral library make the process easy to use, yet it also includes powerful tools for data reduction, spectral search, and spectral mapping. • Some statistical tools such as statistical filtering and using bi- variety regression analysis were suggested to get reliable results.

37 Summary: • Hyperspectral image analysis can be a very powerful tool for cost effective analysis of minerals, identifying mineral abundances and mapping the geological characteristics of an area.In our approach, we acquire hyperspectral images in the visible light wavelength range when the spatial resolution of such images is required for mineral identification purposes.

This means that we can use relatively cheap and low-noise silicon devices. We are also able to use a UV lamp as a light source rather than a near-IR lamp.