scholarly journals Optimized Performance Parameters for Nighttime Multispectral Satellite Imagery to Analyze Lightings in Urban Areas

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3313 ◽  
Author(s):  
Jasper de Meester ◽  
Tobias Storch
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Near Infrared ◽  
Atmospheric Effects ◽  
Spectral Bands ◽  
Multispectral Satellite Imagery ◽  
Radiation Sources ◽  
Yellow Orange ◽  
Band Combinations

Contrary to its daytime counterpart, nighttime visible and near infrared (VIS/NIR) satellite imagery is limited in both spectral and spatial resolution. Nevertheless, the relevance of such systems is unquestioned with applications to, e.g., examine urban areas, derive light pollution, and estimate energy consumption. To determine optimal spectral bands together with required radiometric and spatial resolution, at-sensor radiances are simulated based on combinations of lamp spectra with typical luminances according to lighting standards, surface reflectances, and radiative transfers for the consideration of atmospheric effects. Various band combinations are evaluated for their ability to differentiate between lighting types and to estimate the important lighting parameters: efficacy to produce visible light, percentage of emissions attributable to the blue part of the spectrum, and assessment of the perceived color of radiation sources. The selected bands are located in the green, blue, yellow-orange, near infrared, and red parts of the spectrum and include one panchromatic band. However, these nighttime bands tailored to artificial light emissions differ significantly from the typical daytime bands focusing on surface reflectances. Compared to existing or proposed nighttime or daytime satellites, the recommended characteristics improve, e.g., classification of lighting types by >10%. The simulations illustrate the feasible improvements in nocturnal VIS/NIR remote sensing which will lead to advanced applications.

scholarly journals Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 5518
Author(s):  
Honglyun Park ◽  
Jaewan Choi
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
False Negative ◽  
Similarity Score ◽  
Majority Voting ◽  
Similarity Analysis ◽  
Spectral Angle Mapper ◽  
Mineral Detection ◽  
Shortwave Infrared

Worldview-3 satellite imagery provides panchromatic images with a high spatial resolution and visible near infrared (VNIR) and shortwave infrared (SWIR) bands with a low spatial resolution. These images can be used for various applications such as environmental analysis, urban monitoring and surveying for sustainability. In this study, mineral detection was performed using Worldview-3 satellite imagery. A pansharpening technique was applied to the spatial resolution of the panchromatic image to effectively utilize the VNIR and SWIR bands of Worldview-3 satellite imagery. The following representative similarity analysis techniques were implemented for the mineral detection: the spectral angle mapper (SAM), spectral information divergence (SID) and the normalized spectral similarity score (NS3). In addition, pixels that could be estimated to indicate minerals were calculated by applying an empirical threshold to each similarity analysis result. A majority voting technique was applied to the results of each similarity analysis and pixels estimated to indicate minerals were finally selected. The results of each similarity analysis were compared to evaluate the accuracy of the proposed methods. From that comparison, it could be confirmed that false negative and false positive rates decreased when the methods proposed in the present study were applied.

Linear spectral mixture modelling of arctic vegetation using ground spectroradiometry

Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Anna Mikheeva ◽  
Anton Novichikhin ◽  
Olga Tutubalina
Keyword(s):  
Satellite Imagery ◽  
Ground Level ◽  
Spectral Unmixing ◽  
Atmospheric Effects ◽  
Arctic Vegetation ◽  
Mixture Modelling ◽  
New Information ◽  
Level Data ◽  
Multispectral Satellite Imagery ◽  
Spectral Mixture

ABSTRACTAn experimental linear mixture modelling using ground spectroradiometric measurements in the Kola Peninsula, Russia has been carried out to create a basis for mapping vegetation and non-vegetation components in the tundra-taiga ecotone using satellite imagery. We concentrated on the ground level experiment with the goal to use it further for the classification of multispectral satellite imagery through spectral unmixing. This experiment was performed on the most detailed level of remote sensing research which is free from atmospheric effects and easy to understand. We have measured typical ecotone components, including Cetraria nivalis, Betula tortuosa, Empetrum nigrum, Betula nana, Picea abies and rocks (nepheline syenite). The result of the experiment shows that the spectral mixture is indeed formed linearly but different components have different influence. Typical spectral thresholds for each component were found which are significant for vegetation mapping. Spectral unmixing of ground level data was performed and accuracy was estimated. The results add new information on typical spectral thresholds which can potentially be applied for multispectral satellite imagery when upscaling from high resolution to coarser resolution.

scholarly journals A COMPARATIVE ASSESSMENT OF EFFICACY OF SUPER RESOLVED AIRBORNE HYPERSPECTRAL OUTPUTS IN URBAN MATERIAL AND LAND COVER INFORMATION EXTRACTION

2018 ◽  
Vol XLII-5 ◽  
pp. 653-658 ◽  
Author(s):  
K. Mishra ◽  
A. Siddiqui ◽  
V. Kumar
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Urban Areas ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Spatial Information ◽  
Confusion Matrix ◽  
Spectral Characteristics ◽  
Support Vector ◽  
Spectral Angle Mapper

<p><strong>Abstract.</strong> Urban areas despite being heterogeneous in nature are characterized as mixed pixels in medium to coarse resolution imagery which renders their mapping as highly inaccurate. A detailed classification of urban areas therefore needs both high spatial and spectral resolution marking the essentiality of different satellite data. Hyperspectral sensors with more than 200 contiguous bands over a narrow bandwidth of 1&amp;ndash;10<span class="thinspace"></span>nm can distinguish identical land use classes. However, such sensors possess low spatial resolution. As the exchange of rich spectral and spatial information is difficult at hardware level resolution enhancement techniques like super resolution (SR) hold the key. SR preserves the spectral characteristics and enables feature visualization at a higher spatial scale. Two SR algorithms: Anchored Neighbourhood Regression (ANR) and Sparse Regression and Natural Prior (SRP) have been executed on an airborne hyperspectral scene of Advanced Visible/Near Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) for the mixed environment centred on Kankaria Lake in the city of Ahmedabad thereby bringing down the spatial resolution from 8.1<span class="thinspace"></span>m to 4.05<span class="thinspace"></span>m. The generated super resolved outputs have been then used to map ten urban material and land cover classes identified in the study area using supervised Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods. Visual comparison and accuracy assessment on the basis of confusion matrix and Pearson’s Kappa coefficient revealed that SRP super-resolved output classified using radial basis function (RBF) kernel based SVM is the best outcome thereby highlighting the superiority of SR over simple scaling up and resampling approaches.</p>

The fog/low stratus clouds in the Arctic: detection with multispectral satellite imagery

2020 ◽  
Author(s):  
Daria Tatsii ◽  
Natalia Fedoseeva
Keyword(s):  
Satellite Imagery ◽  
Complex Structure ◽  
Far Infrared ◽  
The Arctic ◽  
Underlying Surface ◽  
Polar Regions ◽  
Stratus Clouds ◽  
Spectral Bands ◽  
Multispectral Satellite Imagery ◽  
Important Challenge

&lt;p&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; The safe operation of aviation and shipping, particularly in areas of insufficient coverage of automatic meteorological stations in the Arctic requires accurate interpretation of satellite images. Operational detection of fog and low stratus clouds and recognizing of them on the background of snow and ice cover and cloudiness of the upper layer is very important challenge.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; &amp;#160;The verified images obtained by Aqua and Terra satellites with a scanning radiometer MODIS, which operates in 36 spectral bands, with wavelengths from 0.4 &amp;#181;m to 14.4 &amp;#181;m, were collected.&amp;#160; With the Beam VISAT 5.0 software, which was designed to work with satellite data in raster format, thematic digital techniques of satellite multispectral information, based on difference in the values of the integral brightness of the images, both in optical and far-infrared ranges of the spectrum, have been developed. &amp;#160;These techniques, models of additive color synthesis, improve the quality of interpretation of fogs and low stratus clouds in terms of the complex structure of cloudiness and underlying surface in polar regions. Developed RGB combinations, which are based on the selected MODIS bands are:&lt;/p&gt;&lt;ol&gt;&lt;li&gt;RGB (1.6 &amp;#181;m; 0.8 &amp;#181;m; 0.6 &amp;#181;m)&lt;/li&gt; &lt;li&gt;RGB (0.8 &amp;#181;m; 3.9-8.7 &amp;#181;m; 10.8 &amp;#181;m)&lt;/li&gt; &lt;li&gt;RGB (0.8 &amp;#181;m; 1.6 &amp;#181;m; 3.9-8.7 &amp;#181;m)&lt;/li&gt; &lt;li&gt;RGB ((0-12)-(0-11) &amp;#181;m, (0-11)-(0-3.8) &amp;#181;m, (0-11) &amp;#181;m)&lt;/li&gt; &lt;/ol&gt;&lt;p&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; Analysis of the obtained images has shown that the developed models of color synthesis help to distinguish the fog/low stratus clouds under different conditions of cloudiness and underlying surface accurately.&lt;/p&gt;&lt;p&gt;Key words: remote sensing, satellite imagery, additive color synthesis, fog, low stratus clouds, polar regions&lt;/p&gt;

scholarly journals Suspended Particulate Matter Concentrations Retrieved from Self-Calibrated Multispectral Satellite Imagery

2011 ◽  
Vol 59 (4) ◽  
pp. 251-261 ◽  
Author(s):  
Milan Onderka ◽  
Marek Rodný ◽  
Yvetta Velísková
Keyword(s):  
Remote Sensing ◽  
Particulate Matter ◽  
Satellite Imagery ◽  
Suspended Particulate Matter ◽  
Optical Model ◽  
Near Infrared ◽  
Remotely Sensed Data ◽  
Suspended Particulate ◽  
Multispectral Satellite Imagery ◽  
Visual Domain

Suspended Particulate Matter Concentrations Retrieved from Self-Calibrated Multispectral Satellite ImageryInland waters are known to be laden with high levels of suspended particulate matter (SPM). Remotely sensed data have been shown to provide a true synoptic view of SPM over vast areas. However, as to date, there is no universal technique that would be capable of retrieving SPM concentrations without a complete reliance on time-consuming and costly ground measurements ora prioriknowledge of inherent optical properties of water-borne constituents. The goal of this paper is to present a novel approach making use of the synergy found between the reflectance in the visual domain (~ 400-700 nm) with the near-infrared portion of the spectrum (~ 700-900 nm). The paper begins with a brief discourse of how the shape and spectral dependence of reflectance is determined by high concentrations of SPM. A modeled example is presented to mimic real-world conditions in fluvial systems, with specific absorption and scattering coefficients of the virtual optically active constituents taken from the literature. Using an optical model, we show that in the visual spectral domain (~ 400-700 nm) the water-leaving radiance responds to increasing SPM (0-100 g m-3) in a non-linear manner. Contrarily to the visual spectra, reflectance in the near infrared domain (~ 700-900 nm) appears to be almost linearly related to a broad range of SPM concentrations. To reduce the number of parameters, the reflectance function (optical model) was approximated with a previously experimentally verified exponential equation (Schiebeet al., 1992: Remote sensing of suspended sediments: the Lake Chicot, Arkansas project, Int. J. Remote Sensing,13, 8, 1487-1509). The SPM term in Schiebe's equation was expressed as a linear function of top-of-atmosphere reflectance. This made it possible to calibrate the reflectance in the visual domain by reflectance values from the near-IR portion of the spectrum. The possibility to retrieve SPM concentrations from only remote sensing data without any auxiliary ground mea-surements is tested on a Landsat ETM + scene acquired over a reservoir with moderately turbid water with SPM concentrations between 15-70 g m-3. The retrieved concentrations (on average) differ from in-situ measurement by ~ 10.5 g m-3.

Comparison of Evaluation Based on Different Atmospheric Correction Methods for HJ-1A Hyperspectral Imaging Data

2011 ◽  
Vol 108 ◽  
pp. 224-229 ◽  
Author(s):  
Ping Wang ◽  
Zhu Rong Xing ◽  
You Gui Feng
Keyword(s):  
Image Quality ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Atmospheric Correction ◽  
Atmospheric Effects ◽  
Imaging Data ◽  
Spectral Bands ◽  
Radiation Induced

HJ-1 Hyperspectral image radiometer is on the HJ-1A satellite. It will provide images about 115 spectral bands between 0.45 and 0.95μm, with spatial resolution of 100 meters and give a return visit every 96 hours. Atmospheric correction models of 6S and FLAASH were applied to the image. The results showed that both models could carry out the radiation correction to radiation-induced distortion caused by atmospheric effects, but correction results were different. 6S had well results comparing to the in-situ spectrum of vegetation. The image quality was better which had the further application ability.

scholarly journals SOME ASPECTS OF SATELLITE IMAGERY INTEGRATION FROM EROS B AND LANDSAT 8

2016 ◽  
Vol XLI-B7 ◽  
pp. 647-652
Author(s):  
A. Fryskowska ◽  
M. Wojtkowska ◽  
P. Delis ◽  
A. Grochala
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Thermal Infrared ◽  
Visual Assessment ◽  
Landsat 8 ◽  
Image Integration ◽  
Panchromatic Image ◽  
False Color ◽  
New Sensors ◽  
Band Combinations

The Landsat 8 satellite which was launched in 2013 is a next generation of the Landsat remote sensing satellites series. It is equipped with two new sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). What distinguishes this satellite from the previous is four new bands (coastal aerosol, cirrus and two thermal infrared TIRS bands). Similar to its antecedent, Landsat 8 records electromagnetic radiation in a panchromatic band at a range of 0.5&dash;0.9 μm with a spatial resolution equal to 15 m. In the paper, multispectral imagery integration capabilities of Landsat 8 with data from the new high resolution panchromatic EROS B satellite are analyzed. The range of panchromatic band for EROS B is 0.4&dash;0.9 μm and spatial resolution is 0.7 m. Research relied on improving the spatial resolution of natural color band combinations (bands: 4,3,2) and of desired false color band composition of Landsat 8 satellite imagery. For this purpose, six algorithms have been tested: Brovey’s, Mulitplicative, PCA, IHS, Ehler's, HPF. On the basis of the visual assessment, it was concluded that the best results of multispectral and panchromatic image integration, regardless land cover, are obtained for the multiplicative method. These conclusions were confirmed by statistical analysis using correlation coefficient, ERGAS and R-RMSE indicators.

scholarly journals UAV-Derived Multispectral Bathymetry

2020 ◽  
Vol 12 (23) ◽  
pp. 3897
Author(s):  
Lorenzo Rossi ◽  
Irene Mammi ◽  
Filippo Pelliccia
Keyword(s):  
Shallow Waters ◽  
Multibeam Echosounder ◽  
Bathymetric Data ◽  
Spectral Bands ◽  
Sea Bottom ◽  
Digital Elevation ◽  
Multispectral Satellite Imagery ◽  
Aerial Vehicle ◽  
Marine Applications ◽  
Band Combinations

Bathymetry is considered an important component in marine applications as several coastal erosion monitoring and engineering projects are carried out in this field. It is traditionally acquired via shipboard echo sounding, but nowadays, multispectral satellite imagery is also commonly applied using different remote sensing-based algorithms. Satellite-Derived Bathymetry (SDB) relates the surface reflectance of shallow coastal waters to the depth of the water column. The present study shows the results of the application of Stumpf and Lyzenga algorithms to derive the bathymetry for a small area using an Unmanned Aerial Vehicle (UAV), also known as a drone, equipped with a multispectral camera acquiring images in the same WorldView-2 satellite sensor spectral bands. A hydrographic Multibeam Echosounder survey was performed in the same period in order to validate the method’s results and accuracy. The study area was approximately 0.5 km2 and located in Tuscany (Italy). Because of the high percentage of water in the images, a new methodology was also implemented for producing a georeferenced orthophoto mosaic. UAV multispectral images were processed to retrieve bathymetric data for testing different band combinations and evaluating the accuracy as a function of the density and quantity of sea bottom control points. Our results indicate that UAV-Derived Bathymetry (UDB) permits an accuracy of about 20 cm to be obtained in bathymetric mapping in shallow waters, minimizing operative expenses and giving the possibility to program a coastal monitoring surveying activity. The full sea bottom coverage obtained using this methodology permits detailed Digital Elevation Models (DEMs) comparable to a Multibeam Echosounder survey, and can also be applied in very shallow waters, where the traditional hydrographic approach requires hard fieldwork and presents operational limits.

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