scholarly journals A Study on Simple Calibration Method for Airborne Hyperspectral Sensor Data using Skylight reference Data

2006 ◽  
Vol 45 (4) ◽  
pp. 47-53
Author(s):  
Tomoyuki SUHAMA ◽  
Atsushi RIKIMARU ◽  
Kazuyoshi TAKAHASHI
Keyword(s):  
Reference Data ◽  
Calibration Method ◽  
Sensor Data ◽  
Hyperspectral Sensor

Multiple data parameter identification for nonlinear conceptual models

1997 ◽  
Vol 36 (5) ◽  
pp. 61-68 ◽  
Author(s):  
Hermann Eberl ◽  
Amar Khelil ◽  
Peter Wilderer
Keyword(s):  
Optimization Problem ◽  
Conceptual Models ◽  
Reference Data ◽  
Transport Model ◽  
Calibration Method ◽  
Data Sets ◽  
Multiple Data ◽  
Marquardt Algorithm ◽  
Multicriteria Optimization Problem ◽  
Higher Order Differential Equations

A numerical method for the identification of parameters of nonlinear higher order differential equations is presented, which is based on the Levenberg-Marquardt algorithm. The estimation of the parameters can be performed by using several reference data sets simultaneously. This leads to a multicriteria optimization problem, which will be treated by using the Pareto optimality concept. In this paper, the emphasis is put on the presentation of the calibration method. As an example identification of the parameters of a nonlinear hydrological transport model for urban runoff is included, but the method can be applied to other problems as well.

scholarly journals Vegetation mapping using airborne hyperspectral sensor data measured in early summer

2007 ◽  
Vol 46 (5) ◽  
pp. 43-55 ◽  
Author(s):  
Hiroshi P. SATO ◽  
Mamoru KOARAI ◽  
Satoshi MIYASAKA ◽  
Hajime MAKITA ◽  
Hiroshi YAGI
Keyword(s):  
Vegetation Mapping ◽  
Early Summer ◽  
Sensor Data ◽  
Hyperspectral Sensor

scholarly journals Estimation of Bacterial Pustule on Soybean Using Crane-Mounted Hyperspectral Sensor Data

2007 ◽  
Vol 46 (6) ◽  
pp. 16-24 ◽  
Author(s):  
Naoko KOSAKA ◽  
Yohei MINEKAWA ◽  
Kuniaki UTO ◽  
Yukio KOSUGI ◽  
Kunio ODA ◽  
...  
Keyword(s):  
Sensor Data ◽  
Hyperspectral Sensor

scholarly journals A Rapid Transfer Alignment Method for SINS Based on the Added Backward-Forward SINS Resolution and Data Fusion

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Yiting Liu ◽  
Lihui Wang
Keyword(s):  
Data Fusion ◽  
Reference Data ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Alignment Method ◽  
Initial Alignment ◽  
Transfer Alignment ◽  
Gyro Bias ◽  
Sensor Errors ◽  
Rapid Transfer

Two viewpoints are given: (1) initial alignment of strapdown inertial navigation system (SINS) can be fulfilled with a set of inertial sensor data; (2) estimation time for sensor errors can be shortened by repeated data fusion on the added backward-forward SINS resolution results and the external reference data. Based on the above viewpoints, aiming to estimate gyro bias in a shortened time, a rapid transfer alignment method, without any changes for Kalman filter, is introduced. In this method, inertial sensor data and reference data in one reference data update cycle are stored, and one backward and one forward SINS resolutions are executed. Meanwhile, data fusion is executed when the corresponding resolution ends. With the added backward-forward SINS resolution, in the above mentioned update cycle, the estimating operations for gyro bias are added twice, and the estimation time for it is shortened. In the ship swinging condition, with the “velocity plus yaw” matching, the effectiveness of this method is proved by the simulation.

scholarly journals A Novel AVM Calibration Method Using Unaligned Square Calibration Boards

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2265
Author(s):  
Jung Hyun Lee ◽  
Dong-Wook Lee
Keyword(s):  
Coordinate System ◽  
Reference Data ◽  
Side Information ◽  
Reference Sample ◽  
Likelihood Estimation ◽  
Calibration Method ◽  
World Coordinate System ◽  
Sample Data ◽  
The World ◽  
Left And Right

An around view monitoring (AVM) system acquires the front, rear, left, and right-side information of a vehicle using four cameras and transforms the four images into one image coordinate system to monitor around the vehicle with one image. Conventional AVM calibration utilizes the maximum likelihood estimation (MLE) to determine the parameters that can transform the captured four images into one AVM image. The MLE requires reference data of the image coordinate system and the world coordinate system to estimate these parameters. In conventional AVM calibration, many aligned calibration boards are placed around the vehicle and are measured to extract the reference sample data. However, accurately placing and measuring the calibration boards around a vehicle is an exhaustive procedure. To remediate this problem, we propose a novel AVM calibration method that requires only four randomly placed calibration boards by estimating the location of each calibration board. First, we define the AVM errors and determine the parameters that minimize the error in estimating the location. We then evaluate the accuracy of the proposed method through experiments using a real-sized vehicle and an electric vehicle for children to show that the proposed method can generate an AVM image similar to the conventional AVM calibration method regardless of a vehicle’s size.

scholarly journals Depth and Width Changeable Network-Based Deep Kernel Learning-Based Hyperspectral Sensor Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Liu ◽  
Tingting Wang ◽  
Yulong Qiao
Keyword(s):  
Data Analysis ◽  
Network Architecture ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Kernel Learning ◽  
Discriminative Ability ◽  
Neural Network Learning ◽  
And Performance ◽  
Hyperspectral Sensor

Sensor data analysis is used in many application areas, for example, Artificial Intelligence of Things (AIoT), with the rapid developing of the deep neural network learning that promotes its application area. In this work, we propose the Depth and Width Changeable Deep Kernel Learning-based hyperspectral sensing data analysis algorithm. Compared with the traditional kernel learning-based hyperspectral data classification, the proposed method has its advantages on the hyperspectral data classification. With the deep kernel learning, the feature is mapped through many times mapping and has the more discriminative ability. So, the deep kernel learning has the better performance compared with the multiple kernels learning. And it has the ability to adjust the network architecture for hyperspectral data space, with the optimization equation of the span bound. The experiments are implemented to testified the feasibility and performance of the algorithms on the hyperspectral data analysis, with the classification accuracy of hyperspectral data. The comprehensive analysis of the experiments shows that the proposed algorithm is feasible to hyperspectral sensor data analysis and its promising classification method in many areas data analysis.

scholarly journals A systematic MEMS sensor calibration framework

2015 ◽  
Vol 4 (1) ◽  
pp. 97-102 ◽  
Author(s):  
A. Dickow ◽  
G. Feiertag
Keyword(s):  
Pressure Sensors ◽  
Calibration Method ◽  
Least Square ◽  
Sensor Data ◽  
Sensor Calibration ◽  
Calibration Point ◽  
Point Sets ◽  
Mems Sensor ◽  
Least Square Fit

Abstract. In this paper we present a systematic method to determine sets of close to optimal sensor calibration points for a polynomial approximation. For each set of calibration points a polynomial is used to fit the nonlinear sensor response to the calibration reference. The polynomial parameters are calculated using ordinary least square fit. To determine the quality of each calibration, reference sensor data is measured at discrete test conditions. As an error indicator for the quality of a calibration the root mean square deviation between the calibration polynomial and the reference measurement is calculated. The calibration polynomials and the error indicators are calculated for all possible calibration point sets. To find close to optimal calibration point sets, the worst 99% of the calibration options are dismissed. This results in a multi-dimensional probability distribution of the probably best calibration point sets. In an experiment, barometric MEMS (micro-electromechanical systems) pressure sensors are calibrated using the proposed calibration method at several temperatures and pressures. The framework is applied to a batch of six of each of the following sensor types: Bosch BMP085, Bosch BMP180, and EPCOS T5400. Results indicate which set of calibration points should be chosen to achieve good calibration results.

scholarly journals Star-Based Calibration of the Installation Between the Camera and Star Sensor of the Luojia 1-01 Satellite

2019 ◽  
Vol 11 (18) ◽  
pp. 2081 ◽  
Author(s):  
Zhichao Guan ◽  
Yonghua Jiang ◽  
Jingyin Wang ◽  
Guo Zhang
Keyword(s):  
High Frequency ◽  
Road Network ◽  
Reference Data ◽  
Calibration Method ◽  
Star Sensor ◽  
Control Points ◽  
Positioning Accuracy ◽  
Ground Control Points ◽  
Position Data ◽  
Frequency Calibration

Ground control points (GCPs) are generally used to calibrate the installation between the camera and star sensor of a satellite in orbit and improve the geometric positioning accuracy of the satellite. However, the use of GCPs for high-frequency calibration is difficult, and it is particularly difficult to acquire accurate GCPs for the image of a nightlight satellite. In this study, we developed a camera-star sensor installation calibration method that eliminates the need for GCPs. In the proposed method, the camera and star sensor lenses are simultaneously pointed at the star, and the camera-star sensor installation is accurately calibrated by processing the star map obtained by the camera and star sensors. Reference data such as road network and Moon position data were used to verify the proposed method and evaluate its positioning accuracy. The results of the application of the method to the positioning of the Luojia 1-01 satellite indicated an accuracy within 800 m, which is comparable with that of the traditional method.

Discrimination of tomato plants under different irrigation regimes: analysis of hyperspectral sensor data

2014 ◽  
Vol 26 (2) ◽  
pp. 77-88 ◽  
Author(s):  
M. Rinaldi ◽  
A. Castrignanò ◽  
D. De Benedetto ◽  
D. Sollitto ◽  
S. Ruggieri ◽  
...  
Keyword(s):  
Sensor Data ◽  
Tomato Plants ◽  
Irrigation Regimes ◽  
Hyperspectral Sensor
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