scholarly journals Radiometric Calibration of UAV Remote Sensing Image with Spectral Angle Constraint

2019 ◽  
Vol 11 (11) ◽  
pp. 1291 ◽  
Author(s):  
Kaiqiu Xu ◽  
Yan Gong ◽  
Shenghui Fang ◽  
Ke Wang ◽  
Zhiheng Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Linear Method ◽  
Radiometric Calibration ◽  
Constraint Condition ◽  
Infrared Band ◽  
Spectral Bands ◽  
Quantitative Remote Sensing ◽  
Spectral Channels ◽  
Spectral Angle

In recent years, the acquisition of high-resolution multi-spectral images by unmanned aerial vehicles (UAV) for quantitative remote sensing research has attracted more and more attention, and radiometric calibration is the premise and key to the quantification of remote sensing information. The traditional empirical linear method independently calibrates each channel, ignoring the correlation between spectral bands. However, the correlation between spectral bands is very valuable information, which becomes more prominent as the number of spectral channels increases. Based on the empirical linear method, this paper introduces the constraint condition of spectral angle, and makes full use of the information of each band for radiometric calibration. The results show that, compared with the empirical linear method, the proposed method can effectively improve the accuracy of radiometric calibration, with the improvement range of Mean Relative Percent Error (MRPE) being more than 3% in the range of visible band and within 1% in the range of near-infrared band. Besides, the method has great advantages in agricultural remote sensing quantitative inversion.

Rapid dehazing algorithm based on large-scale median filtering for high-resolution visible near-infrared remote sensing images

2014 ◽  
Vol 12 (05) ◽  
pp. 1461010 ◽  
Author(s):  
Changmiao Hu ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Near Infrared ◽  
Low Frequency ◽  
Median Filtering ◽  
Remote Sensing Images ◽  
Infrared Band ◽  
Remote Sensing Image Processing

In recent years, China's demand for satellite remote sensing images increased. Thus, the country launched a series of satellites equipped with high-resolution sensors. The resolutions of these satellites range from 30 m to a few meters, and the spectral range covers the visible to the near-infrared band. These satellite images are mainly used for environmental monitoring, mapping, land surface classification and other fields. However, haze is an important factor that often affects image quality. Thus, dehazing technology is becoming a critical step in high-resolution remote sensing image processing. This paper presents a rapid algorithm for dehazing based on a semi-physical haze model. Large-scale median filtering technique is used to extract large areas of bright, low-frequency information from images to estimate the distribution and thickness of the haze. Four images from different satellites are used for experiment. Results show that the algorithm is valid, fast, and suitable for the rapid dehazing of numerous large-sized high-resolution remote sensing images in engineering applications.

RealSens™: An Advanced Passive Airborne Natural Gas Leak Detection Technology

2006 ◽  
Author(s):  
Adrian Banica ◽  
Chris K. Sheard ◽  
Boyd T. Tolton
Keyword(s):  
Remote Sensing ◽  
Natural Gas ◽  
Near Infrared ◽  
New Technology ◽  
Infrared Band ◽  
Underground Pipelines ◽  
Detection Technology ◽  
Gas Leak ◽  
Gas Leaks ◽  
Transmission And Distribution

Detecting natural gas leaks from the worlds nearly 5 million kilometers of underground pipelines is a difficult and costly challenge. Existing technologies are limited to ground deployment and have a number of limitations such as slow response, false leak readings and high costs. Various remote sensing solutions have been proposed in the past and a few are currently being developed. This paper starts by describing the remote sensing concept and then will focus on a new technology developed by Synodon scientists. This airborne instrument is a passive Gas Filter Correlation Radiometer (GFCR) that is tuned to measure ethane in the 3.3 microns near-infrared band. With its target natural gas column sensitivity of 50 μm, the instrument is capable of detecting very small leaks in the range of 5–10 cuft/hr in winds that exceed 6 miles/hr. The paper concludes with a description of the service which Synodon will be offering to the transmission and distribution pipeline operators using the new technology.

Results of Field Trials of Realsens™, An Airborne Natural Gas Leak Detection Technology

2008 ◽  
Author(s):  
Adrian Banica ◽  
Doug Miller ◽  
Boyd T. Tolton
Keyword(s):  
Remote Sensing ◽  
Natural Gas ◽  
Near Infrared ◽  
New Technology ◽  
Field Tests ◽  
Field Trials ◽  
Infrared Band ◽  
Detection Technology ◽  
Gas Leak ◽  
Gas Leaks

Detecting natural gas leaks from the worlds nearly 5 million kilometers of underground pipelines is a difficult and costly challenge. Existing technologies are limited to ground deployment and have a number of limitations such as slow response, false leak readings and high costs. Various remote sensing solutions have been proposed in the past and a few are currently being developed. This paper starts by describing the remote sensing concept and then will focus on a new technology developed by Synodon scientists. This airborne instrument is a passive Gas Filter Correlation Radiometer (GFCR) that is tuned to measure ethane in the 3.3 microns near-infrared band. The paper will then present the results of the first airborne field tests and conclude with a description of the service which Synodon will be offering to the transmission and distribution pipeline operators using the new technology.

scholarly journals Wheat Crop Acreage Estimation Based on Remote Sensing and GIS in Jabalpur (Madhya Pradesh, India)

2021 ◽  
pp. 88-94
Author(s):  
Umakant Rawat ◽  
Ankit Yadav ◽  
P.S. Pawar ◽  
Aniket Rajput ◽  
Devendra Vasht ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Unsupervised Classification ◽  
Good Choice ◽  
Madhya Pradesh ◽  
Wheat Crop ◽  
Kappa Statistics ◽  
Crop Acreage ◽  
Spectral Bands ◽  
Sentinel 2

Mapping and classification crop by using satellite images is a challenging task that can minimize the complexities of field visits. The recently launched Sentinel-2 satellite has thirteen spectral bands, short revisit time and determination at three different resolutions (10 m, 20 m and 60 m), besides that, the free availability of the images makes it a good choice for vegetation mapping. This study aims to classify crop, using single date Sentinel-2 imagery within the Jabalpur, state of Madhya Pradesh, India. The classification was performed by using Unsupervised Classification. In this study, four spectral bands, i.e., Near Infrared, Red, Green, and Blue of Sentinel-2 were stacked for the classification. The results show that the area of wheat crop corresponds to 83.07%; Gram/ Pulses, 14.64%; and other crop, 2.28%. The overall accuracy and overall Kappa Statistics of the classification using Sentinel-2 imagery are 85.71% and 0.819%, respectively. Therefore, this study has found that Sentinel-2 presented great potential in the mapping of the agriculture areas of Jabalpur by remote sensing.

scholarly journals Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4453 ◽  
Author(s):  
Mamaghani ◽  
Salvaggio
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Laboratory Method ◽  
Sensor Calibration ◽  
Radiometric Calibration ◽  
End User ◽  
Sensing Applications ◽  
A Value ◽  
Remote Sensing Applications

This paper focuses on the calibration of multispectral sensors typically used for remote sensing. These systems are often provided with "factory" radiometric calibration and vignette correction parameters. These parameters, which are assumed to be accurate when the sensor is new, may change as the camera is utilized in real-world conditions. As a result, regular calibration and characterization of any sensor should be conducted. An end-user laboratory method for computing both the vignette correction and radiometric calibration function is discussed in this paper. As an exemplar, this method for radiance computation is compared to the method provided by MicaSense for their RedEdge series of sensors. The proposed method and the method provided by MicaSense for radiance computation are applied to a variety of images captured in the laboratory using a traceable source. In addition, a complete error propagation is conducted to quantify the error produced when images are converted from digital counts to radiance. The proposed methodology was shown to produce lower errors in radiance imagery. The average percent error in radiance was −10.98%, −0.43%, 3.59%, 32.81% and −17.08% using the MicaSense provided method and their "factory" parameters, while the proposed method produced errors of 3.44%, 2.93%, 2.93%, 3.70% and 0.72% for the blue, green, red, near infrared and red edge bands, respectively. To further quantify the error in terms commonly used in remote sensing applications, the error in radiance was propagated to a reflectance error and additionally used to compute errors in two widely used parameters for assessing vegetation health, NDVI and NDRE. For the NDVI example, the ground reference was computed to be 0.899 ± 0.006, while the provided MicaSense method produced a value of 0.876 ± 0.005 and the proposed method produced a value of 0.897 ± 0.007. For NDRE, the ground reference was 0.455 ± 0.028, MicaSense method produced 0.239 ± 0.026 and the proposed method produced 0.435 ± 0.038.

scholarly journals Calibration and Data Quality Assurance Technical Advancements for Quantitative Remote Sensing in the DRAGON 4 Project

2021 ◽  
Vol 13 (24) ◽  
pp. 4996
Author(s):  
Lingling Ma ◽  
Yongguang Zhao ◽  
Chuanrong Li ◽  
Philippe Goryl ◽  
Cheng Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Local Governments ◽  
Control Strategies ◽  
Remote Sensing Data ◽  
Sensor Calibration ◽  
Radiometric Calibration ◽  
Sensor Performance ◽  
Quantitative Remote Sensing ◽  
Remote Sensor ◽  
Set Up

Robust calibration and validation (Cal and Val) should guarantee the accuracy of the retrieved information, make the remote sensing data consistent and traceable, and maintain the sensor performance during the operational phase. The DRAGON program has set up many remote sensing research topics on various application domains. In order to promote the effectiveness of data modeling and interpretation, it is necessary to solve various challenges in Cal and Val for quantitative RS applications. This project in the DRAGON 4 program aims to promote the cooperation of the Cal and Val experts from European and Chinese institutes in Cal and Val activities, and several achievements have been obtained in the advanced on-orbit optical sensor calibration, as well as microwave remote sensor calibration and product generation. The outcomes of the project have benefited the related remote sensing modeling and product retrieval, and promoted the radiometric calibration network (RadCalNet) as an international operational network for calibration, intercalibration, and validation. Moreover, this project provided local governments with a more accurate OMI NO2 data in China, which were used to study the air quality control during APEC period, Parade period and G20 period. This will be of ongoing be value for monitoring atmospheric environmental quality and formulating pollution control strategies.

scholarly journals Índice de vegetação ajustado ao solo (IVAS): estado da arte e suas potencialidades (Adjusted Vegetation Index to Solo (SAVI): state of the art and its potential)

2016 ◽  
Vol 9 (6) ◽  
pp. 2054
Author(s):  
Gabrielle de Araújo Ribeiro ◽  
João Nailson De Castro Silva ◽  
Janaína Barbosa Da Silva
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
State Of The Art ◽  
Vegetation Indices ◽  
Quantitative Information ◽  
Qualitative And Quantitative ◽  
Spectral Bands ◽  
Satellite Sensors ◽  
Best Parameters

A utilização do Sensoriamento Remoto para a avaliação do meio ambiente é cada vez mais aplicado em pesquisas. As imagens adquiridas pelos sensores acoplados aos satélites fornecem dados qualitativos e quantitativos do estado da vegetação através da aplicação dos índices de vegetação. Os índices são obtidos pela combinação matemáticas das reflectâncias dos alvos nas faixas espectrais, principalmente do vermelho e infravermelho próximo e podem ser afetados por diferentes fatores tais como reflectância, irradiancia e o brilho do solo. Um dos índices comumente utilizados, principalmente em áreas semiáridas, onde se tem influencia do brilho do solo, é o índice de vegetação ajustado ao solo (IVAS). Este índice introduz um fator de ajuste (L) ao índice de vegetação normalizada (IVDN) para minimizar os efeitos da presença do solo. Porém para cada região deve-se estudar e determinar os melhores parâmetros para o mesmo. Portanto este trabalho tem como objetivo apresentar uma revisão de literatura em relação ao índice de vegetação ajustado ao solo em diferentes biomas brasileiro e outras aplicações.   A B S T R A C T The use of remote sensing for environmental assessment is increasingly applied in research. The images acquired by the satellite sensors coupled to provide qualitative and quantitative information on the state of the vegetation by the application of vegetation indices. The indices are obtained by mathematical combination of the reflectance of the targets in the spectral bands, especially the red and near infrared and can be affected by different factors such as reflectance, irradiance and the brightness of the soil. One of the commonly used indices, especially in semi-arid areas where it has influence of soil brightness, is the vegetation index adjusted to the ground (UAI). This index introduces an adjustment factor (L) normalized vegetation index (NDVI) to minimize the effects of soil present. However, for each region should study and determine the best parameters for the same. Therefore this work aims to present a literature review regarding the vegetation index adjusted to the soil in different Brazilian biomes and other applications. Keywords : Remote Sensing; vegetation index; spectral analysis, biome.   

scholarly journals CCD CBERS and ASTER data in dasometric characterization of Pinus radiata D. Don (North-western Spain)

CERNE ◽  
2013 ◽  
Vol 19 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Eva Sevillano-Marco ◽  
Alfonso Fernández-Manso ◽  
Carmen Quintano ◽  
Marcela Poulain
Keyword(s):  
Remote Sensing ◽  
Pinus Radiata ◽  
Near Infrared ◽  
Thermal Emission ◽  
Correlation Coefficients ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Infrared Band ◽  
Western Spain

A Chinese-Brazilian Earth Resources Satellite (CBERS) and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes coupled with ancillary georeferenced data and field survey were employed to examine the potential of the remote sensing data in stand basal area, volume and aboveground biomass assessment over large areas of Pinus radiata D. Don plantations in Northwestern Spain. Statistical analysis proved that the near infrared band and the shade fraction image showed significant correlation coefficients with all stand variables considered. Predictive models were accordingly selected and utilized to undertake the spatial distribution of stand variables in radiata stands delimited by the National Forestry Map. The study reinforces the potentiality of remote sensing techniques in a cost-effective assessment of forest systems.

scholarly journals Application of UAV-Based Multi-Angle Hyperspectral Remote Sensing in Fine Vegetation Classification

2019 ◽  
Vol 11 (23) ◽  
pp. 2753 ◽  
Author(s):  
Yan ◽  
Deng ◽  
Liu ◽  
Zhu
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Hot Spot ◽  
Hyperspectral Remote Sensing ◽  
Vegetation Classification ◽  
Principal Plane ◽  
Dark Spot ◽  
Infrared Band ◽  
Blue Band ◽  
Better Than

To obtain a high-accuracy vegetation classification of high-resolution UAV images, in this paper, a multi-angle hyperspectral remote sensing system was built using a six-rotor UAV and a Cubert S185 frame hyperspectral sensor. The application of UAV-based multi-angle remote sensing in fine vegetation classification was studied by combining a bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods. This method can not only effectively reduce the classification phenomena that influence different objects with similar spectra, but also benefit the construction of a canopy-level BRDF. Then, the importance of the BRDF characteristic parameters are discussed in detail. The results show that the overall classification accuracy (OA) of the vertical observation reflectance based on BRDF extrapolation (BRDF_0°) (63.9%) was approximately 24% higher than that based on digital orthophoto maps (DOM) (39.8%), and kappa using BRDF_0° was 0.573, which was higher than that using DOM (0.301); a combination of the hot spot and dark spot features, as well as model features, improved the OA and kappa to around 77% and 0.720, respectively. The reflectance features near hot spots were more conducive to distinguishing maize, soybean, and weeds than features near dark spots; the classification results obtained by combining the observation principal plane (BRDF_PP) and on the cross-principal plane (BRDF_CP) features were best (OA = 89.2%, kappa = 0.870), and especially, this combination could improve the distinction among different leaf-shaped trees. BRDF_PP features performed better than BRDF_CP features. The observation angles in the backward reflection direction of the principal plane performed better than those in the forward direction. The observation angles associated with the zenith angles between −10° and −20° were most favorable for vegetation classification (solar position: zenith angle 28.86°, azimuth 169.07°) (OA was around 75%–80%, kappa was around 0.700–0.790); additionally, the most frequently selected bands in the classification included the blue band (466 nm–492 nm), green band (494 nm–570 nm), red band (642 nm–690 nm), red edge band (694 nm–774 nm), and the near-infrared band (810 nm–882 nm). Overall, the research results promote the application of multi-angle remote sensing technology in vegetation information extraction and provide important theoretical significance and application value for regional and global vegetation and ecological monitoring.

scholarly journals Mangrove Vegetation Health Assessment Based on Remote Sensing Indices for Tanjung Piai, Malay Peninsular

2019 ◽  
Vol 12 (2) ◽  
pp. 26-40
Author(s):  
Sheriza Mohd Razali ◽  
Ahmad Ainuddin Nuruddin ◽  
Marryanna Lion
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Health Assessment ◽  
Stem Density ◽  
Infrared Band ◽  
Green Vegetation ◽  
Conservation Activity ◽  
Ground Truthing

Abstract Mangroves critically require conservation activity due to human encroachment and environmental unsustainability. The forests must be conserving through monitoring activities with an application of remote sensing satellites. Recent high-resolution multispectral satellite was used to produce Normalized Difference Vegetation Index (NDVI) and Tasselled Cap transformation (TC) indices mapping for the area. Satellite Pour l’Observation de la Terre (SPOT) SPOT-6 was employed for ground truthing. The area was only a part of mangrove forest area of Tanjung Piai which estimated about 106 ha. Although, the relationship between the spectral indices and dendrometry parameters was weak, we found a very significant between NDVI (mean) and stem density (y=10.529x + 12.773) with R2=0.1579. The sites with NDVI calculated varied from 0.10 to 0.26 (P1 and P2), under the environmental stress due to sand deposition found was regard as unhealthy vegetation areas. Whereas, site P5 with NDVI (mean) 0.67 is due to far distance from risk wave’s zone, therefore having young/growing trees with large lush green cover was regard as healthy vegetation area. High greenness indicated in TC means, the bands respond to a combination of high absorption of chlorophyll in the visible bands and the high reflectance of leaf structures in the near-infrared band, which is characteristic of healthy green vegetation. Overall, our study showed our tested WV-2 image combined with ground data provided valuable information of mangrove health assessment for Tanjung Piai, Johor, Malay Peninsula.

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