Composite color images of aerial gamma‐ray spectrometric data

Geophysics ◽  
1983 ◽  
Vol 48 (6) ◽  
pp. 722-735 ◽  
Author(s):  
Joseph S. Duval
Keyword(s):  
Gamma Ray ◽  
Color Image ◽  
Mineral Exploration ◽  
Color Images ◽  
South Texas ◽  
Geologic Mapping ◽  
Partial Synthesis ◽  
Data Set ◽  
Spectrometric Data ◽  
Sample Data

Aerial gamma‐ray data provide estimates of the apparent surface concentrations of potassium (K), equivalent uranium (eU), and equivalent thorium (eTh). These data can be expressed as nine radiometric parameters: K, eU, eTh, eU/eTh, eU/K, eTh/K, eTh/eU, K/eU, and K/eTh. The U.S. Geological Survey (USGS) has developed a technique which combines any three of these parameters to form a composite color image. The color image provides a partial synthesis of the radiometric data that can be used to aid geologic mapping and mineral exploration. The sample data set, from the Freer area in south Texas, illustrates the use of the color images.

Statistical analysis of airborne gamma‐ray data for geologic mapping purposes: Crixas‐Itapaci area, Goias, Brazil

Geophysics ◽  
1989 ◽  
Vol 54 (10) ◽  
pp. 1326-1332 ◽  
Author(s):  
A. C. B. Pires ◽  
N. Harthill
Keyword(s):  
Statistical Analysis ◽  
Gamma Ray ◽  
Mineral Exploration ◽  
Aerial Survey ◽  
Geologic Mapping ◽  
Good Correspondence ◽  
Gamma Ray Spectrometer ◽  
Data Points ◽  
Spectrometer Data ◽  
Mode Technique

Q‐mode factor analysis, K‐means clustering, and G‐mode clustering were used on digitized gamma‐ray spectrometer data from an aerial survey of the Crixas‐Itapaci area, Goias, Brazil. The data points including seven variables—eU, eTh, K, total count, U/Th, U/K, and Th/K—were digitized for a 2 km square grid. For the northwest corner of the area the data were gridded at 1 km. The Q‐mode classification method supplied results that do not show a good correspondence with the known geology. The K‐means clustering procedure barely identified the main lithologic features of the area. The G‐mode technique produced results that correlate well with the known geology and identified the greenstone belts present in the area by discriminating their ultramafic and mafic components from adjacent felsic rocks. Statistical analysis of aerial gamma‐ray spectrometer data can be very helpful in mapping geologic units in poorly known areas. It can also be used for mineral exploration purposes if mineralization is known to be associated with lithologies that can be identified by the techniques used in this study.

HIGH SENSITIVITY GAMMA‐RAY SPECTROMETRY—STATE OF THE ART AND TRIAL APPLICATION OF FACTOR ANALYSIS

Geophysics ◽  
1977 ◽  
Vol 42 (3) ◽  
pp. 549-559 ◽  
Author(s):  
Joseph S. Duval
Keyword(s):  
Factor Analysis ◽  
Gamma Ray ◽  
State Of The Art ◽  
Mineral Exploration ◽  
High Sensitivity ◽  
Original Data ◽  
Gamma Ray Spectrometry ◽  
Similar Data ◽  
Spectrometric Data ◽  
Other Information

The remote sensing of terrestrial gamma rays has application in geologic mapping, mineral exploration, reactor site monitoring, location of lost radioactive sources, measurement of the water equivalence of snow, and soil mapping. Although the state of the art is quite good, there is a need to reexamine the use of detectors other than thallium activated sodium iodide detectors (e.g., plastic scintillators) to improve the corrections used for altitude variations and to present the data as apparent concentrations of potassium, uranium, and thorium rather than as counts per unit of time. In an attempt to improve data analysis, the technique known as factor analysis has been applied to airborne gamma‐ray spectrometric data from a survey in South Texas. This analysis technique allows the geologist/geophysicist to perform a coordinate transformation from the four count rates [potassium (K), equivalent uranium (eU), equivalent thorium (eTh), and total count] and the three ratios, (eU/K, eU/eTh, eTh/K) to a system of three independent coordinates. These three coordinates are constrained to reproduce the total variance of the original data, and the data can be separated into groups using the criterion that similar data points have similar coordinates. The distribution of the separated groups can be mapped for comparison with other information such as the mapped geology. This map of the groups represents a synthesis of all of the radiometric data.

Utilization of airborne gamma ray spectrometric data for geological mapping and radioactive mineral exploration of Gabel Umm Tineidba area, south eastern desert, Egypt

2015 ◽  
Vol 12 (2) ◽  
pp. 149-160 ◽  
Author(s):  
Tharwat H. Abdel Hafeez ◽  
Mohamed A. S. Youssef ◽  
Waheed H. Mohamed
Keyword(s):  
Gamma Ray ◽  
Mineral Exploration ◽  
Eastern Desert ◽  
Geological Mapping ◽  
Gamma Ray Spectrometry ◽  
Spectrometric Data ◽  
South Eastern ◽  
Uranium Exploration ◽  
Geological Information ◽  
Surface Geology

The present work utilizes airborne gamma ray spectrometric data in a trial to refine surface geology of igneous, metamorphic and sedimentary rocks, detect any radioactive mineralization at Gabel Umm Tineidba area South Eastern Desert, Egypt. The study area is covered by rock exposures ranging in age from the Precambrian to Quaternary. Airborne gamma ray spectrometry can be very helpful in mapping surface geology. This provides estimates of the apparent surface concentrations of the most common naturally occurring radioactive elements, such as potassium (K), equivalent uranium (eU) and equivalent thorium (eTh). This is based on the assumption that, the absolute and relative concentrations of these radioelements vary measurably and significantly with lithology. The composite image technique is used to display simultaneously three parameters of the three radioelement concentrations and their three binary ratios on one image. The technique offers much in terms of lithological discrimination, based on color differences and showed efficiency in defining areas, where different lithofacies occur within areas mapped as one continuous lithology. The integration between surface geological information and geophysical data led to detailing the surface geology and the contacts between different rock units. Significant locations or favourable areas for uranium exploration are defined, where the measurements exceed (X+2S), taking X as the arithmetic mean of eU, eU/eTh and eU/K measurements and S as the standard deviation corresponding to each variables. The study area shows the presence of fifteen relatively high uraniferous zone. In addition, the trend analysis based on the total count map and the published geological map shows that, most of the well-developed structural lineaments have NS, ENE, NNE and NNW trends.

Using the image grand tour to visualize fluvial deltaic architectural elements in south Texas, USA

2013 ◽  
Vol 1 (1) ◽  
pp. SA117-SA129 ◽  
Author(s):  
Bradley C. Wallet
Keyword(s):  
Spatial Information ◽  
Principal Component ◽  
Color Images ◽  
South Texas ◽  
Data Set ◽  
Grand Tour ◽  
Architectural Elements ◽  
Multiple Structures ◽  
Land Survey ◽  
Single Data

Spectral decomposition can produce dozens of attributes for a single data set, far exceeding the ability for direct visualization. Some solutions have been proposed. The state-of-the-art approach is via the use of principal component analysis. However, this approach has significant inherent weaknesses, such as a lack of inclusion of spatial information and a tendency to inflate noise. Previous work has shown the ability of the image grand tour to construct lower-dimensional views of spectral information resulting in multiple images showing distinct architectural components. I propose a novel workflow for constructing color images to display multiple structures simultaneously. These images are constructed in a way that makes them complementary, leading to rich color images that are useful for interpretation. I demonstrate the value of this workflow though application to a land survey over Tertiary channels from south Texas.

Grand Canyon riverbed sediment changes, experimental release of September 2000 - a sample data set

2003 ◽  
Author(s):  
Florence L. Wong ◽  
Roberto J. Anima ◽  
Peter Galanis ◽  
Jennifer Codianne ◽  
Yu Xia ◽  
...  
Keyword(s):  
Grand Canyon ◽  
Data Set ◽  
Sample Data ◽  
Riverbed Sediment ◽  
Experimental Release

New Set of Invariant Quaternion Krawtchouk Moments for Color Image Representation and Recognition

2021 ◽  
pp. 2250037
Author(s):  
Gaber Hassan ◽  
Khalid M. Hosny ◽  
R. M. Farouk ◽  
Ahmed M. Alzohairy
Keyword(s):  
Quaternion Algebra ◽  
Color Image ◽  
Image Representation ◽  
Color Images ◽  
Color Channel ◽  
Krawtchouk Polynomials ◽  
Krawtchouk Moments ◽  
Similarity Transformations ◽  
The Stability ◽  
Discrete Moments

One of the most often used techniques to represent color images is quaternion algebra. This study introduces the quaternion Krawtchouk moments, QKrMs, as a new set of moments to represent color images. Krawtchouk moments (KrMs) represent one type of discrete moments. QKrMs use traditional Krawtchouk moments of each color channel to describe color images. This new set of moments is defined by using orthogonal polynomials called the Krawtchouk polynomials. The stability against the translation, rotation, and scaling transformations for QKrMs is discussed. The performance of the proposed QKrMs is evaluated against other discrete quaternion moments for image reconstruction capability, toughness against various types of noise, invariance to similarity transformations, color face image recognition, and CPU elapsed times.

Investigation of Regression-Based Effect Size Methods Developed in Single-Subject Studies

2021 ◽  
pp. 014544552110540
Author(s):  
Nihal Sen
Keyword(s):  
Effect Size ◽  
Experimental Studies ◽  
Single Subject ◽  
Single Subject Design ◽  
Data Set ◽  
Design Studies ◽  
Sample Data ◽  
The Difference ◽  
D Values ◽  
Effect Size Calculation

The purpose of this study is to provide a brief introduction to effect size calculation in single-subject design studies, including a description of nonparametric and regression-based effect sizes. We then focus the rest of the tutorial on common regression-based methods used to calculate effect size in single-subject experimental studies. We start by first describing the difference between five regression-based methods (Gorsuch, White et al., Center et al., Allison and Gorman, Huitema and McKean). This is followed by an example using the five regression-based effect size methods and a demonstration how these methods can be applied using a sample data set. In this way, the question of how the values obtained from different effect size methods differ was answered. The specific regression models used in these five regression-based methods and how these models can be obtained from the SPSS program were shown. R2 values obtained from these five methods were converted to Cohen’s d value and compared in this study. The d values obtained from the same data set were estimated as 0.003, 0.357, 2.180, 3.470, and 2.108 for the Allison and Gorman, Gorsuch, White et al., Center et al., as well as for Huitema and McKean methods, respectively. A brief description of selected statistical programs available to conduct regression-based methods was given.

Human Skin Detection in Color Images Using Deep Learning

2015 ◽  
Vol 5 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Human Skin ◽  
Skin Color ◽  
Color Image ◽  
Gaussian Model ◽  
Color Images ◽  
Skin Detection ◽  
Learning Methods ◽  
Rule Based

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.

Sign in / Sign up

Export Citation Format

Share Document