Development of the technique for selection of operating spectral bands for three-channel pyrometric device of two spectral ratios

2011 ◽  
Author(s):  
Nadezhda Y. Tupikina ◽  
Eugene V. Sypin
Keyword(s):  
Spectral Ratios ◽  
Spectral Bands ◽  
Selection Of

Evaluating the Potential of Clonal Selection Optimization Algorithm to Hyperspectral Image Feature Selection

2012 ◽  
Vol 500 ◽  
pp. 799-805 ◽  
Author(s):  
Farhad Samadzadegan ◽  
Shahin Rahmatollahi Namin ◽  
Mohammad Ali Rajabi
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Hyperspectral Image ◽  
Clonal Selection ◽  
Fitness Function ◽  
Hyperspectral Images ◽  
Image Feature ◽  
Spectral Bands ◽  
Selection Algorithms ◽  
Selection Of

The great number of captured near spectral bands in hyperspectral images causes the curse of dimensionality problem and results in low classification accuracy. The feature selection algorithms try to overcome this problem by limiting the input space dimensions of classification for hyperspectral images. In this paper, immune clonal selection optimization algorithm is used for feature selection. Also one of the fastest Artificial Immune classification algorithms is used to compute fitness function of the feature selection. The comparison of the feature selection results with genetic algorithm shows the clonal selection’s higher performance to solve selection of features.

Multi-method ensemble selection of spectral bands related to leaf biochemistry

2015 ◽  
Vol 164 ◽  
pp. 57-65 ◽  
Author(s):  
Hannes Feilhauer ◽  
Gregory P. Asner ◽  
Roberta E. Martin
Keyword(s):  
Spectral Bands ◽  
Ensemble Selection ◽  
Selection Of ◽  
Leaf Biochemistry

Selection of spectral bands for Indian remote sensing satellite (IRS)

1983 ◽  
Vol 3 (2) ◽  
pp. 283-286 ◽  
Author(s):  
K.L. Majumder ◽  
R. Ramakrishnan ◽  
I.C. Matieda ◽  
G. Sharma ◽  
A.K.S. Gopalan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Indian Remote Sensing ◽  
Indian Remote Sensing Satellite ◽  
Spectral Bands ◽  
Remote Sensing Satellite ◽  
Selection Of

scholarly journals Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

2019 ◽  
Vol 11 (11) ◽  
pp. 1298 ◽  
Author(s):  
Ahmed Laamrani ◽  
Aaron A. Berg ◽  
Paul Voroney ◽  
Hannes Feilhauer ◽  
Line Blackburn ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Least Squares Regression ◽  
Agricultural Field ◽  
Southern Ontario ◽  
Spectral Bands ◽  
Selection Of

The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400–1000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000–2500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711–715, 727, 986–998, and 433–435 nm regions (VNIR); and 2365–2373, 2481–2500, and 2198–2206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors.

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