Results 21 to 30 of about 45,417 (317)

Spectral Feature Selection from the Hyperspectral Dataset to Identify Pistachio Leaves Infected by Psylla [PDF]

open access: yesJournal of Agricultural Machinery, 2022
IntroductionPistachio production has been adversely affected by Psylla, which is a devastating insect. The primary goal of this study was to select sensitive spectral bands to distinguish pistachio leaves infected by Psylla from healthy leaves. Diagnosis
A Moghimi, A Sazgarnia, M. H Aghkhani
doaj   +1 more source

Customizing kernel functions for SVM-based hyperspectral image classification [PDF]

open access: yes, 2008
Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms.
Damper, R. I.   +7 more
core   +1 more source

Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery [PDF]

open access: yes, 2009
This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing ...
Moussaoui, Saïd   +10 more
core   +1 more source

Nonlinear spectral unmixing of hyperspectral images using Gaussian processes [PDF]

open access: yes, 2012
This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components ...
Altmann, Yoann   +5 more
core   +1 more source

Nonlinear unmixing of hyperspectral images using a generalized bilinear model [PDF]

open access: yes, 2011
Nonlinear models have recently shown interesting properties for spectral unmixing. This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization not only of
Altmann, Yoann   +9 more
core   +1 more source

Detecting the Sources of Methane Emission from Oil Shale Mining and Processing Using Airborne Hyperspectral Data

open access: yesRemote Sensing, 2020
Methane (CH4) is one of important greenhouse gases that affects the global radiative balance after carbon dioxide (CO2). Previous studies have demonstrated the detection of known sources of CH4 emission using the hyperspectral technology based on in situ
Chunlei Xiao   +4 more
doaj   +1 more source

Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging

open access: yesDiagnostics, 2023
Problem: Similarity measures are widely used as an approved method for spectral discrimination or identification with their applications in different areas of scientific research.
Priya Pathak   +10 more
doaj   +1 more source

Residual component analysis of hyperspectral images - Application to joint nonlinear unmixing and nonlinearity detection [PDF]

open access: yes, 2014
This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional ...
Altmann, Yoann   +10 more
core   +1 more source

MEETNet: Morphology-Edge Enhanced Triple-Cascaded Network for Infrared Small Target Detection

open access: yesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Infrared small target detection is focused on accurately identifying tiny targets with low signal-to-noise ratio against complex backgrounds, representing a critical challenge in the field of infrared image processing. Existing approaches frequently fail
Enyu Zhao   +4 more
doaj   +1 more source

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

open access: yesRemote Sensing, 2017
Traditional imagery—provided, for example, by RGB and/or NIR sensors—has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can ...
Telmo Adão   +6 more
doaj   +1 more source

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