scholarly journals Assessment of the Single-Mixture Gas Assumption for the Correlated K-Distribution Fictitious Gas Method in H2O–CO2–CO Mixture at High Temperature

2008 ◽  
Vol 130 (10) ◽  
Author(s):  
C. Caliot ◽  
G. Flamant ◽  
M. El Hafi ◽  
Y. Le Maoult
Keyword(s):  
Remote Sensing ◽  
Path Length ◽  
Narrow Band ◽  
Specific Area ◽  
Sensitivity Study ◽  
Computational Time ◽  
Line Of Sight ◽  
Spectral Bands ◽  
Integrated Intensity ◽  
Atmospheric Path

This paper deals with the comparison of spectral narrow band models based on the correlated-K (CK) approach in the specific area of remote sensing of plume signatures. The CK models chosen may or may not include the fictitious gas (FG) idea and the single-mixture-gas assumption (SMG). The accuracy of the CK and the CK-SMG as well as the CKFG and CKFG-SMG models are compared, and the influence of the SMG assumption is inferred. The errors induced by each model are compared in a sensitivity study involving the plume thickness and the atmospheric path length as parameters. This study is conducted in two remote-sensing situations with different absolute pressures at sea level (105Pa) and at high altitude (16.6km, 104Pa). The comparisons are done on the basis of the error obtained for the integrated intensity while leaving a line of sight that is computed in three common spectral bands: 2000–2500cm−1, 3450–3850cm−1, and 3850–4150cm−1. In most situations, the SMG assumption induces negligible differences. Furthermore, compared to the CKFG model, the CKFG-SMG model results in a reduction of the computational time by a factor of 2.

scholarly journals Spectral Imagery Tensor Decomposition for Semantic Segmentation of Remote Sensing Data through Fully Convolutional Networks

2020 ◽  
Vol 12 (3) ◽  
pp. 517 ◽  
Author(s):  
Josué López ◽  
Deni Torres ◽  
Stewart Santos ◽  
Clement Atzberger
Keyword(s):  
Remote Sensing ◽  
Computational Complexity ◽  
Performance Metrics ◽  
Semantic Segmentation ◽  
Input Image ◽  
Computational Time ◽  
Convolutional Network ◽  
Data Set ◽  
Input Space ◽  
Spectral Bands

This work aims at addressing two issues simultaneously: data compression at input space and semantic segmentation. Semantic segmentation of remotely sensed multi- or hyperspectral images through deep learning (DL) artificial neural networks (ANN) delivers as output the corresponding matrix of pixels classified elementwise, achieving competitive performance metrics. With technological progress, current remote sensing (RS) sensors have more spectral bands and higher spatial resolution than before, which means a greater number of pixels in the same area. Nevertheless, the more spectral bands and the greater number of pixels, the higher the computational complexity and the longer the processing times. Therefore, without dimensionality reduction, the classification task is challenging, particularly if large areas have to be processed. To solve this problem, our approach maps an RS-image or third-order tensor into a core tensor, representative of our input image, with the same spatial domain but with a lower number of new tensor bands using a Tucker decomposition (TKD). Then, a new input space with reduced dimensionality is built. To find the core tensor, the higher-order orthogonal iteration (HOOI) algorithm is used. A fully convolutional network (FCN) is employed afterwards to classify at the pixel domain, each core tensor. The whole framework, called here HOOI-FCN, achieves high performance metrics competitive with some RS-multispectral images (MSI) semantic segmentation state-of-the-art methods, while significantly reducing computational complexity, and thereby, processing time. We used a Sentinel-2 image data set from Central Europe as a case study, for which our framework outperformed other methods (included the FCN itself) with average pixel accuracy (PA) of 90% (computational time ∼90s) and nine spectral bands, achieving a higher average PA of 91.97% (computational time ∼36.5s), and average PA of 91.56% (computational time ∼9.5s) for seven and five new tensor bands, respectively.

scholarly journals How can remote sensing techniques help monitoring the vine and maximize the terroir potential?

2018 ◽  
Vol 50 ◽  
pp. 02007
Author(s):  
Cecile Tondriaux ◽  
Anne Costard ◽  
Corinne Bertin ◽  
Sylvie Duthoit ◽  
Jérôme Hourdel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Sampling Strategy ◽  
Wine Quality ◽  
Wine Production ◽  
Spectral Bands ◽  
High Resolution Satellite Images ◽  
Remote Sensing Techniques ◽  
Set Up

In each winegrowing region, the winegrower tries to value its terroir and the oenologists do their best to produce the best wine. Thanks to new remote sensing techniques, it is possible to implement a segmentation of the vineyard according to the qualitative potential of the vine stocks and make the most of each terroir to improve wine quality. High resolution satellite images are processed in several spectral bands and algorithms set-up specifically for the Oenoview service allow to estimate vine vigour and a heterogeneity index that, used together, directly reflect the vineyard oenological potential. This service is used in different terroirs in France (Burgundy, Languedoc, Bordeaux, Anjou) and in other countries (Chile, Spain, Hungary and China). From this experience, we will show how remote sensing can help managing vine and wine production in all covered terroirs. Depending on the winegrowing region and its specificities, its use and results present some differences and similarities that we will highlight. We will give an overview of the method used, the advantage of implementing field intra-or inter-selection and how to optimize the use of amendment and sampling strategy as well as how to anticipate the whole vineyard management.

Specific spectral bands for different land cover contexts to improve the efficiency of remote sensing archaeological prospection: The Arpi case study

2009 ◽  
Vol 10 ◽  
pp. e41-e48 ◽  
Author(s):  
Cristiana Bassani ◽  
Rosa Maria Cavalli ◽  
Roberto Goffredo ◽  
Angelo Palombo ◽  
Simone Pascucci ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Archaeological Prospection ◽  
Spectral Bands

LAI Retrieval Based on PROSPECT-SAILH Model from Multi-Angular Data of WiDAS Imaging System

2012 ◽  
Vol 518-523 ◽  
pp. 5697-5703
Author(s):  
Zhao Yan Liu ◽  
Ling Ling Ma ◽  
Ling Li Tang ◽  
Yong Gang Qian
Keyword(s):  
Remote Sensing ◽  
Imaging System ◽  
Remote Sensing Data ◽  
Canopy Reflectance ◽  
Area Index ◽  
Sensing Data ◽  
Spectral Bands ◽  
Sensing Technology ◽  
Leaf Optical Properties ◽  
Angular Data

The aim of this study is to assess the capability of estimating Leaf Area Index (LAI) from high spatial resolution multi-angular Vis-NIR remote sensing data of WiDAS (Wide-Angle Infrared Dual-mode Line/Area Array Scanner) imaging system by inverting the coupled radiative transfer models PROSPECT-SAILH. Based on simulations from SAILH canopy reflectance model and PROSPECT leaf optical properties model, a Look-up Table (LUT) which describes the relationship between multi-angular canopy reflectance and LAI has been produced. Then the LAI can be retrieved from LUT by directly matching canopy reflectance of six view directions and four spectral bands with LAI. The inversion results are validated by field data, and by comparing the retrieval results of single-angular remote sensing data with multi-angular remote sensing data, we can found that the view angle takes the obvious impact on the LAI retrieval of single-angular data and that high accurate LAI can be obtained from the high resolution multi-angular remote sensing technology.

Integrity Assessment of a Free-Fall Lifeboat Launched From a FPSO

2015 ◽  
Author(s):  
Guomin Ji ◽  
Nabila Berchiche ◽  
Sébastien Fouques ◽  
Thomas Sauder ◽  
Svein-Arne Reinholdtsen
Keyword(s):  
Finite Element ◽  
Pressure Distribution ◽  
Structural Integrity ◽  
Dynamic Effect ◽  
Sensitivity Study ◽  
Computational Time ◽  
Load Factor ◽  
Time Varying ◽  
Integrity Assessment

The paper addresses the structural integrity assessment of lifeboat launched from floating production, storage and offloading (FPSO) vessels. The study is based on long-term drop lifeboat simulations accounting for more than 50 years of hindcast data of metocean conditions and corresponding FPSO motions. Selection of the load cases and strength analyses with high computational time is a challenge. The load cases analyzed are those corresponding to the 99th percentile of long term distribution of indicators for large slamming loads (CARXZ) or large submergence (Imaxsub). For six selected cases, the time-varying pressure distribution on the lifeboat hull during and after water impact is calculated by CFD simulations using StarCCM+. The finite element model (FEM) of the composite structure of the lifeboat is modelled by ABAQUS. Quasi-static finite element (FE) analyses are performed for the selected load cases. The structural integrity is assessed by the maximum stress and Tsai-Wu failure measure. In the present study, the load and resistance factors are combined and applied to the response. A sensitivity study is performed to investigate the non-linear load/response effects when the load factor is applied to the load. In addition, dynamic analysis is performed with the time-varying pressure distribution for selected case and the dynamic effect is investigated.

scholarly journals Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control

2021 ◽  
Author(s):  
P. L. Arun ◽  
R Mathusoothana S Kumar
Keyword(s):  
Remote Sensing ◽  
Conditional Probability ◽  
Traffic Control ◽  
Road Safety ◽  
Image Inpainting ◽  
Computational Time ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Non Linear ◽  
Occlusion Removal

AbstractOcclusion removal is a significant problem to be resolved in a remote traffic control system to enhance road safety. However, the conventional techniques do not recognize traffic signs well due to the vehicles are occluded. Besides occlusion removal was not performed in existing techniques with a less amount of time. In order to overcome such limitations, Non-linear Gaussian Bilateral Filtered Sorenson–Dice Exemplar Image Inpainting Based Bayes Conditional Probability (NGBFSEII-BCP) Method is proposed. Initially, a number of remote sensing images are taken as input from Highway Traffic Dataset. Then, the NGBFSEII-BCP method applies the Non-Linear Gaussian Bilateral Filtering (NGBF) algorithm for removing the noise pixels in input images. After preprocessing, the NGBFSEII-BCP method is used to remove the occlusion in the input images. Finally, NGBFSEII-BCP Method applies Bayes conditional probability to find operation status and thereby gets higher road safety using remote sensing images. The technique conducts the simulation evaluation using metrics such as peak signal to noise ratio, computational time, and detection accuracy. The simulation result illustrates that the NGBFSEII-BCP Method increases the detection accuracy by 20% and reduces the computation time by 32% as compared to state-of-the-art works.

Experimental Validation of Forced Response Methods in a Multi-Stage Axial Turbine

2018 ◽  
Author(s):  
Thomas Hauptmann ◽  
Christopher E. Meinzer ◽  
Joerg R. Seume
Keyword(s):  
Experimental Data ◽  
Vibration Amplitude ◽  
Turbine Blades ◽  
Service Condition ◽  
Sensitivity Study ◽  
Forced Response ◽  
Tip Clearance ◽  
Computational Time ◽  
Reference Case ◽  
Axial Turbine

Depending on the in service condition of jet engines, turbine blades may have to be replaced, refurbished, or repaired in the course of an engine overhaul. Thus, significant changes of the turbine blade geometry can be introduced due to regeneration and overhaul processes. Such geometric variances can affect the aerodynamic and aeroelastic behavior of turbine blades. One goal in the development of the regeneration process is to estimate the aerodynamic excitation of turbine blades depending on these geometric variances caused during the regeneration. Therefore, this study presents an experimentally validated comparison of two methods for the prediction of forced response in a multistage axial turbine. Two unidirectional fluid structure interaction (FSI) methods, a time-linearized and a time-accurate with a subsequent linear harmonic analysis, are employed and the results validated against experimental data. The results show that the vibration amplitude of the time-linearized method is in good agreement with the experimental data and, also requires lower computational time than the time-accurate FSI. Based on this result, the time-linearized method is used to perform a sensitivity study of the tip clearance size of the last rotor blade row of the five stage axial turbine. The results show that an increasing tip clearances size causes an up to 1.35 higher vibration amplitude compared to the reference case, due to increased forcing and decreased damping work.

scholarly journals Multiangular Polarized Characteristics of Optically Thin Cirrus in the Visible and Near-Infrared Spectral Region

2010 ◽  
Vol 67 (3) ◽  
pp. 749-768 ◽  
Author(s):  
T. H. Cheng ◽  
X. F. Gu ◽  
L. F. Chen ◽  
T. Yu ◽  
G. L. Tian
Keyword(s):  
Optical Thickness ◽  
Near Infrared ◽  
Global Climate ◽  
Sensitivity Study ◽  
Radiation Budget ◽  
Sensitivity Index ◽  
Transfer Program ◽  
Conceptual Approach ◽  
Spectral Bands ◽  
Polarized Reflectance

Abstract Optically thin cirrus play a key role in the earth’s radiation budget and global climate change. Their radiative effects depend critically on the thin cirrus optical and microphysical properties. In this paper, inhomogeneous hexagonal monocrystals (IHMs), which consist of a pure hexagon with spherical air bubble or aerosol inclusions, are applied to calculate the single-scattering properties of individual ice crystals. The multiangular polarized characteristics of optically thin cirrus for the 0.865- and 1.38-μm spectral bands are simulated on the basis of an adding–doubling radiative transfer program. The sensitivity of total and polarized reflectance at the top of the atmosphere (TOA) to different aerosol, cirrus, and surface parameters is studied. A new sensitivity index is introduced to further quantify the sensitivity study. The TOA polarized reflectance measured by the Polarization and Directionality of the Earth’s Reflectance (POLDER) instruments is compared to simulated TOA total and polarized reflectance. The test results are reasonable, although small deviations caused by the change of aerosol properties and thin cirrus optical thickness do exist. Finally, on the basis of the sensitivity study, a conceptual approach is suggested to simultaneously retrieve thin cirrus clouds’ optical thickness, ice particle shape, and the underlying aerosol optical thickness using the TOA total and polarized reflectance of the 0.865- and 1.38-μm spectral bands measured at multiple viewing angles.

scholarly journals Payload Configurations for Efficient Image Acquisition – Indian Perspective

2014 ◽  
Vol XL-8 ◽  
pp. 1201-1206 ◽  
Author(s):  
D. R. M. Samudraiah ◽  
M. Saxena ◽  
S. Paul ◽  
P. Narayanababu ◽  
S. Kuriakose ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
High Efficiency ◽  
Image Acquisition ◽  
Image Data ◽  
Space Research ◽  
Remotely Sensed Data ◽  
Data Utilization ◽  
Spectral Bands ◽  
The World

The world is increasingly depending on remotely sensed data. The data is regularly used for monitoring the earth resources and also for solving problems of the world like disasters, climate degradation, etc. Remotely sensed data has changed our perspective of understanding of other planets. With innovative approaches in data utilization, the demands of remote sensing data are ever increasing. More and more research and developments are taken up for data utilization. The satellite resources are scarce and each launch costs heavily. Each launch is also associated with large effort for developing the hardware prior to launch. It is also associated with large number of software elements and mathematical algorithms post-launch. The proliferation of low-earth and geostationary satellites has led to increased scarcity in the available orbital slots for the newer satellites. Indian Space Research Organization has always tried to maximize the utility of satellites. Multiple sensors are flown on each satellite. In each of the satellites, sensors are designed to cater to various spectral bands/frequencies, spatial and temporal resolutions. Bhaskara-1, the first experimental satellite started with 2 bands in electro-optical spectrum and 3 bands in microwave spectrum. The recent Resourcesat-2 incorporates very efficient image acquisition approach with multi-resolution (3 types of spatial resolution) multi-band (4 spectral bands) electro-optical sensors (LISS-4, LISS-3* and AWiFS). The system has been designed to provide data globally with various data reception stations and onboard data storage capabilities. Oceansat-2 satellite has unique sensor combination with 8 band electro-optical high sensitive ocean colour monitor (catering to ocean and land) along with Ku band scatterometer to acquire information on ocean winds. INSAT- 3D launched recently provides high resolution 6 band image data in visible, short-wave, mid-wave and long-wave infrared spectrum. It also has 19 band sounder for providing vertical profile of water vapour, temperature, etc. The same system has data relay transponders for acquiring data from weather stations. The payload configurations have gone through significant changes over the years to increase data rate per kilogram of payload. Future Indian remote sensing systems are planned with very high efficient ways of image acquisition. <br><br> This paper analyses the strides taken by ISRO (Indian Space research Organisation) in achieving high efficiency in remote sensing image data acquisition. Parameters related to efficiency of image data acquisition are defined and a methodology is worked out to compute the same. Some of the Indian payloads are analysed with respect to some of the system/ subsystem parameters that decide the configuration of payload. Based on the analysis, possible configuration approaches that can provide high efficiency are identified. A case study is carried out with improved configuration and the results of efficiency improvements are reported. This methodology may be used for assessing other electro-optical payloads or missions and can be extended to other types of payloads and missions.

scholarly journals Fire Blight Disease Detection for Apple Trees: Hyperspectral Analysis of Healthy, Infected and Dry Leaves

2020 ◽  
Vol 12 (13) ◽  
pp. 2101 ◽  
Author(s):  
Hubert Skoneczny ◽  
Katarzyna Kubiak ◽  
Marcin Spiralski ◽  
Jan Kotlarz ◽  
Artur Mikiciński ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fire Blight ◽  
Vegetation Index ◽  
Pearson Correlation ◽  
Hyperspectral Remote Sensing ◽  
Bacterial Disease ◽  
Apple Trees ◽  
Remote Sensing Technique ◽  
Spectral Bands ◽  
Swir Band

The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight.

Sign in / Sign up

Export Citation Format

Share Document