scholarly journals Determination of target detection limits in hyperspectral data using band selection and dimensionality reduction

2016 ◽  
Author(s):  
W. Gross ◽  
J. Boehler ◽  
K. Twizer ◽  
B. Kedem ◽  
A. Lenz ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Target Detection ◽  
Detection Limits ◽  
Hyperspectral Data ◽  
Band Selection

scholarly journals Assessment of target detection limits in hyperspectral data

2015 ◽  
Author(s):  
W. Gross ◽  
J. Boehler ◽  
H. Schilling ◽  
W. Middelmann ◽  
J. Weyermann ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Limits ◽  
Hyperspectral Data

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

2016 ◽  
Vol 3 (3) ◽  
pp. 503 ◽  
Author(s):  
R. Kiran Kumar ◽  
B. Saichandana ◽  
K. Srinivas
Keyword(s):  
Genetic Algorithm ◽  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Image Data ◽  
Unsupervised Classification ◽  
Hyperspectral Data ◽  
Band Selection ◽  
Data Set ◽  
Mode Decomposition ◽  
Fused Image

<p>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 Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, band selection is done using genetic algorithm in-order to remove bands that convey less information. This dimensionality reduction minimizes many requirements such as storage space, computational load, communication bandwidth etc which is imposed on the unsupervised classification algorithms. Next image fusion is performed on the selected hyperspectral bands to selectively merge the maximum possible features from the selected images to form a single image. This fused image is classified using genetic algorithm. Three different indices, such as K-means Index (KMI) and Jm measure are used as objective functions. This method increases classification accuracy and performance of hyperspectral image than without dimensionality reduction.</p>

Locality Preserving Projection Based on Endmember Extraction for Hyperspectral Image Dimensionality Reduction and Target Detection

2016 ◽  
Vol 70 (9) ◽  
pp. 1573-1581 ◽  
Author(s):  
Yiting Wang ◽  
Shiqi Huang ◽  
Zhigang Liu ◽  
Hongxia Wang ◽  
Daizhi Liu
Keyword(s):  
Dimensionality Reduction ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Data ◽  
Detection Accuracy ◽  
Endmember Extraction ◽  
Locality Preserving Projection ◽  
Locality Preserving ◽  
Weighted Matrix

In order to reduce the effect of spectral variability on calculation precision for the weighted matrix in the locality preserving projection (LPP) algorithm, an improved dimensionality reduction method named endmember extraction-based locality preserving projection (EE-LPP) is proposed in this paper. The method primarily uses the vertex component analysis (VCA) method to extract endmember spectra from hyperspectral imagery. It then calculates the similarity between pixel spectra and the endmember spectra by using the spectral angle distance, and uses it as the basis for selecting neighboring pixels in the image and constructs a weighted matrix between pixels. Finally, based on the weighted matrix, the idea of the LPP algorithm is applied to reduce the dimensions of hyperspectral image data. Experimental results of real hyperspectral data demonstrate that the low-dimensional features acquired by the proposed methods can fully reflect the characteristics of the original image and further improve target detection accuracy.

Determination of non-metallic inclusions in metal alloys by spark atomic emission spectrometry (review)

2018 ◽  
Vol 84 (12) ◽  
pp. 5-19
Author(s):  
D. N. Bock ◽  
V. A. Labusov
Keyword(s):  
Spectral Line ◽  
Internal Standard ◽  
Detection Limits ◽  
Radiation Detectors ◽  
Metal Alloys ◽  
Emission Spectrometry ◽  
Direct Production ◽  
Metallic Inclusions ◽  
Dissolved Form

A review of publications regarding detection of non-metallic inclusions in metal alloys using optical emission spectrometry with single-spark spectrum registration is presented. The main advantage of the method - an extremely short time of measurement (~1 min) – makes it useful for the purposes of direct production control. A spark-induced impact on a non-metallic inclusion results in a sharp increase (flashes) in the intensities of spectral lines of the elements that comprise the inclusion because their content in the metal matrix is usually rather small. The intensity distribution of the spectral line of the element obtained from several thousand of single-spark spectra consists of two parts: i) the Gaussian function corresponding to the content of the element in a dissolved form, and ii) an asymmetric additive in the region of high intensity values ??attributed to inclusions. Their quantitative determination is based on the assumption that the intensity of the spectral line in the single-spark spectrum is proportional to the content of the element in the matter ablated by the spark. Thus, according to the calibration dependence constructed using samples with a certified total element content, it is possible not only to determine the proportions of the dissolved and undissolved element, but also the dimensions of the individual inclusions. However, determination of the sizes is limited to a range of 1 – 20 µm. Moreover, only Al-containing inclusions can be determined quantitatively nowadays. Difficulties occur both with elements hardly dissolved in steels (O, Ca, Mg, S), and with the elements which exhibit rather high content in the dissolved form (Si, Mn). It is also still impossible to determine carbides and nitrides in steels using C and N lines. The use of time-resolved spectrometry can reduce the detection limits for inclusions containing Si and, possibly, Mn. The use of the internal standard in determination of the inclusions can also lower the detection limits, but may distort the results. Substitution of photomultipliers by solid-state linear radiation detectors provided development of more reliable internal standard, based on the background value in the vicinity of the spectral line. Verification of the results is difficult in the lack of standard samples of composition of the inclusions. Future studies can expand the range of inclusions to be determined by this method.

Polarographic and voltammetric determination of 6-mercaptopurine and 6-thioguanine

1986 ◽  
Vol 51 (11) ◽  
pp. 2466-2472 ◽  
Author(s):  
Jiří Barek ◽  
Antonín Berka ◽  
Ludmila Dempírová ◽  
Jiří Zima
Keyword(s):  
Detection Limit ◽  
Differential Pulse Voltammetry ◽  
Detection Limits ◽  
Differential Pulse ◽  
Voltammetric Determination ◽  
Differential Pulse Polarography ◽  
Hanging Mercury Drop Electrode ◽  
Pulse Polarography ◽  
Pulse Voltammetry

Conditions were found for the determination of 6-mercaptopurine (I) and 6-thioguanine (II) by TAST polarography, differential pulse polarography and fast-scan differential pulse voltammetry at a hanging mercury drop electrode. The detection limits were 10-6, 8 . 10-8, and 6 . 10-8 mol l-1, respectively. A further lowering of the detection limit to 2 . 10-8 mol l-1 was attained by preliminary accumulation of the determined substances at the surface of a hanging mercury drop.

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