Predicting buoyant jet characteristics: a machine learning approach
We study positively buoyant miscible jets through high-speed imaging and planar laser-
induced fluorescence methods, and we rely on supervised machine learning techniques to …
induced fluorescence methods, and we rely on supervised machine learning techniques to …
An Unsupervised Feature Extraction Using Endmember Extraction and Clustering Algorithms for Dimension Reduction of Hyperspectral Images
Hyperspectral images (HSIs) provide rich spectral information, facilitating many applications,
including landcover classification. However, due to the high dimensionality of HSIs …
including landcover classification. However, due to the high dimensionality of HSIs …
Optimization of RFM's structure based on PSO algorithm and figure condition analysis
SHA Moghaddam, M Mokhtarzade… - … and Remote Sensing …, 2018 - ieeexplore.ieee.org
Rational function model (RFM) faces difficulty in extracting accurate geometric information
from remotely sensed images, which is mainly due to the problems of overparameterization …
from remotely sensed images, which is mainly due to the problems of overparameterization …
Robust particle swarm optimization of RFMs for high-resolution satellite images based on k-fold cross-validation
S Gholinejad, AA Naeini… - IEEE Journal of …, 2018 - ieeexplore.ieee.org
Rational function model (RFM) is one of the most popular methods of geometrically
correcting high-resolution satellite images (HRSIs). This model encounters …
correcting high-resolution satellite images (HRSIs). This model encounters …
A new approach for geometric correction of UAV-based pushbroom images through the processing of simultaneously acquired frame images
Video-assisted navigation is a strategy that hyperspectral camera manufacturers usually use
to deal with the geometric distortions imposed on raw pushbroom images, due to …
to deal with the geometric distortions imposed on raw pushbroom images, due to …
[HTML][HTML] Rational Polynomial Coefficient Estimation via Adaptive Sparse PCA-Based Method
T Yan, Y Wang, P Wang - Remote Sensing, 2024 - mdpi.com
The Rational Function Model (RFM) is composed of numerous highly correlated Rational
Polynomial Coefficients (RPCs), establishing a mathematical relationship between two …
Polynomial Coefficients (RPCs), establishing a mathematical relationship between two …
An automated PCA-based approach towards optimization of the rational function model
S Gholinejad, A Amiri-Simkooei… - ISPRS Journal of …, 2020 - Elsevier
The existence of several unnecessary and highly correlated coefficients in the rational
function model (RFM), known as rational polynomial coefficients (RPCs), leads to an ill …
function model (RFM), known as rational polynomial coefficients (RPCs), leads to an ill …
Optimization of RFM Problem Using Linearly Programed ℓ₁-Regularization
S Gholinejad, AA Naeini… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Due to the existence of a large number of highly correlated parameters in the rational
function model (RFM), known as rational polynomial coefficients (RPCs), RFM suffers from …
function model (RFM), known as rational polynomial coefficients (RPCs), RFM suffers from …
Optimal regularization method based on the L-curve for solving rational function model parameters
Rational polynomial coefficients in a rational function model (RFM) have high correlation
and redundancy, especially in high-order RFMs, which results in ill-posed problems of the …
and redundancy, especially in high-order RFMs, which results in ill-posed problems of the …
Estimation of rational polynomial coefficients based on singular value decomposition
J Cao, J Fu - Journal of Applied Remote Sensing, 2018 - spiedigitallibrary.org
The rational function model (RFM) has been widely used in space-borne photogrammetry
owing to its simplicity and generality. One of the key issues for using the RFM is to robustly …
owing to its simplicity and generality. One of the key issues for using the RFM is to robustly …