Adaptive Progressive Band Selection for Dimensionality Reduction in Hyperspectral Images

Ettabaa, Karim Saheb; Salem, Manel Ben

doi:10.1007/s12524-017-0691-9

Cited by 15 publications

(6 citation statements)

References 16 publications

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“…CS is able to lower the high price needed to build sensors, at the same time, CS is able to reduce the cost of transfer between the sensor and ground receivers, and the computing cost of the sensor is transferred to the computer on the receiving end. [10][11][12][13] Sparse representation and dictionary learning One of the great achievements of linear algebra is the iterative solution of linear equations. For a matrix A 2 R NÂM ðN < MÞ, there is an underdetermined linear system of equations: Ax ¼ b, in this equation, since the number of unknowns is more than the number of equations, the equation has infinitely many solutions according to conventional methods.…”

Section: Notation and Preliminariesmentioning

confidence: 99%

RETRACTED: A method for remote sensing image denoising based on structured dictionary learning and cloud computing

Yang

Qin

Zhao

2020

International Journal of Electrical Engineering & Education

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With the emerging technology of remote sensing, a huge amount of remote sensing data is collected and stored in the remote sensin02222g platform, and the transmission and processing of data on the platform is extremely wasteful. It is essential to incorporate the speedy remote sensing processing services in an integrated cloud computing architecture. In order to improve the denoising ability of remote sensing image, a new structured dictionary-based method for multispectral image denoising based on cluster is proposed. This method incorporates both the locality of spatial and the correlation across spectrum of multispectral image. Remote sensing image is divided into different groups by clustering, and sparse representation coefficients of spatial and spectral and dictionary is obtained according to the dictionary learning algorithm. After threshold processing, the similar blocks are averaged and realized with multispectral remote sensing image denoising. The algorithm is applied to denoise the noisy remote sensing image of Maoergai area in the upper Minjiang which contain typical vegetation and soil is chosen as study area, simulation results show that higher peak-signal to noise ratio can be obtained as compared to other recent image denoising methods.

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Section: Notation and Preliminariesmentioning

confidence: 99%

RETRACTED: A method for remote sensing image denoising based on structured dictionary learning and cloud computing

Yang

Qin

Zhao

2020

International Journal of Electrical Engineering & Education

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“…Two additional perceptions must be noted. The spectral differences between classes, especially those revealed in the fourteen wavelengths described above, are subtle, as reported elsewhere (DELALIEUX et al, 2007;ETTABAA & SALEM, 2017); in fact, taking as reference the usual range of reflectance values (from zero to unity), the conspicuous differences revealed by the spectrum-ratio technique are of the order of 10 -4 or even smaller. Their detection is due to the extreme signal-to-noise ratio of the Ciência Rural, v.53, n.12, 2023. measurements taken with the equipment presently employed, leading to the significant detection of faint spectral features.…”

Section: Resultssupporting

confidence: 60%

Proximal hyperspectral analysis in grape leaves for region and variety identification

et al. 2023

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Reflectance measurements of plants of the same species can produce sets of data with differences between spectra, due to factors that can be external to the plant, like the environment where the plant grows, and to internal factors, for measurements of different varieties. This paper reports results of the analysis of radiometric measurements performed on leaves of vines of several grape varieties and on several sites. The objective of the research was, after the application of techniques of dimensionality reduction for the definition of the most relevant wavelengths, to evaluate four machine learning models applied to the observational sample aiming to discriminate classes of region and variety in vineyards. The tested machine learning classification models were Canonical Discrimination Analysis (CDA), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Support Vector Machine (SVM). From the results, we reported that the LGBM model obtained better accuracy in spectral discrimination by region, with a value the 0.93, followed by the RF model. Regarding the discrimination between grape varieties, these two models also achieved better results, with accuracies of 0.88 and 0.89. The wavelengths more relevant for discrimination were at ultraviolet, followed by those at blue and green spectral regions. This research pointed toward the importance of defining the wavelengths more relevant to the characterization of the reflectance spectra of leaves of grape varieties and revealed the effective capability of discriminating vineyards by their region or grape variety, using machine learning models.

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“…Data dimensionality reduction refers to using fewer spectral data variables to replace the original variables. The dimensionality reduced data can still reflect the information of the original data and is more conducive to understanding and processing [7]. Hyperspectral data dimensionality reduction can be divided into two: spectral feature selection and spectral feature extraction.…”

Section: Data Dimensionality Reduction and Principal Component Analysismentioning

confidence: 99%

Camouflage Target Recognition Based on Dimension Reduction Analysis of Hyperspectral Image Regions

et al. 2022

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Hyperspectral reconnaissance technology can realize three-dimensional reconnaissance by using target space and spectral information, which effectively improves the efficiency of battlefield reconnaissance. However, in order to obscure what is true and what is false to confuse the enemy, camouflage technology is also developing. Hiding the target in the background environment and setting false targets have become common camouflage procedures on the battlefield. The camouflaged target has very similar spatial and spectral characteristics to the real target, so the method of identifying the camouflaged target according to the similarity threshold of the original spectral data is no longer reliable. In order to solve the problem of high spectral similarity and low discrimination between a camouflaged target and a real target in a hyperspectral image, a joint processing method of spatial spectrum information is adopted in this paper. Firstly, the hyperspectral image is preprocessed, and then the target area to be measured is determined. Finally, the dimensions of the determined sensitive small area are reduced. Experiments show that this processing method can effectively reduce the spectral similarity of true and false targets, increase the spectral difference of true and false targets and improve the ability to identify true and false targets based on hyperspectral images.

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Adaptive Progressive Band Selection for Dimensionality Reduction in Hyperspectral Images

Cited by 15 publications

References 16 publications

RETRACTED: A method for remote sensing image denoising based on structured dictionary learning and cloud computing

RETRACTED: A method for remote sensing image denoising based on structured dictionary learning and cloud computing

Proximal hyperspectral analysis in grape leaves for region and variety identification

Camouflage Target Recognition Based on Dimension Reduction Analysis of Hyperspectral Image Regions

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