scholarly journals Regularization Destriping of Remote Sensing Imagery

2016 ◽  
Author(s):  
Ranil Basnayake ◽  
Erik Bollt ◽  
Nicholas Tufillaro ◽  
Jie Sun ◽  
Michelle Gierach
Keyword(s):  
Infrared Imaging ◽  
Lagrange Equations ◽  
Data Set ◽  
Imaging Spectrometer ◽  
Remote Imaging ◽  
Benchmark Data ◽  
Data Gaps ◽  
Technical Details ◽  
Data Fidelity ◽  
Explicit Finite Difference

Abstract. We illustrate the utility of variational destriping for ocean color images from both mulitspectral and hyperspectral sensors. In particular, we examine data from a filter spectrometer, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (NPP) orbiter, and an airborne grating spectrometer, the Jet Population Laboratory's (JPL) hyperspectral Portable Remote Imaging Spectrometer (PRISM) sensor. We solve the destriping problem using a variational regularization method by giving weights spatially to preserve the other features of the image during the destriping process. The target functional penalizes `the neighborhood of stripes' (strictly, directionally uniform features) while promoting data fidelity, and the functional is minimized by solving the Euler-Lagrange equations with an explicit finite difference scheme. We show the accuracy of our method from a benchmark data set which represents the Sea Surface Temperature off the Coast of Oregon, USA. Technical details, such as how to impose continuity across data gaps using inpainting, are also described.

scholarly journals Regularization destriping of remote sensing imagery

2017 ◽  
Vol 24 (3) ◽  
pp. 367-378 ◽  
Author(s):  
Ranil Basnayake ◽  
Erik Bollt ◽  
Nicholas Tufillaro ◽  
Jie Sun ◽  
Michelle Gierach
Keyword(s):  
Infrared Imaging ◽  
Lagrange Equations ◽  
Data Set ◽  
Imaging Spectrometer ◽  
Remote Imaging ◽  
Benchmark Data ◽  
Data Gaps ◽  
Technical Details ◽  
Data Fidelity ◽  
Explicit Finite Difference

Abstract. We illustrate the utility of variational destriping for ocean color images from both multispectral and hyperspectral sensors. In particular, we examine data from a filter spectrometer, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (NPP) orbiter, and an airborne grating spectrometer, the Jet Population Laboratory's (JPL) hyperspectral Portable Remote Imaging Spectrometer (PRISM) sensor. We solve the destriping problem using a variational regularization method by giving weights spatially to preserve the other features of the image during the destriping process. The target functional penalizes the neighborhood of stripes (strictly, directionally uniform features) while promoting data fidelity, and the functional is minimized by solving the Euler–Lagrange equations with an explicit finite-difference scheme. We show the accuracy of our method from a benchmark data set which represents the sea surface temperature off the coast of Oregon, USA. Technical details, such as how to impose continuity across data gaps using inpainting, are also described.

scholarly journals CLASSIFICATION OF HYPERSPECTRAL DATA BASED ON GUIDED FILTERING AND RANDOM FOREST

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 821-824
Author(s):  
H. Ma ◽  
W. Feng ◽  
X. Cao ◽  
L. Wang
Keyword(s):  
Random Forest ◽  
Infrared Imaging ◽  
Image Filtering ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Data Set ◽  
Imaging Spectrometer ◽  
Guided Image Filter ◽  
Spectral Bands ◽  
Guided Image Filtering

Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to “the curse of dimensionality”. In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA), guided image filtering and the random forest classifier (RF). In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.

scholarly journals The short life of the volcanic island New Late’iki (Tonga) analyzed by multi-sensor remote sensing data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Simon Plank ◽  
Francesco Marchese ◽  
Nicola Genzano ◽  
Michael Nolde ◽  
Sandro Martinis
Keyword(s):  
Infrared Imaging ◽  
Rapid Change ◽  
Monitoring Network ◽  
Remote Sensing Data ◽  
Volcanic Island ◽  
Imaging Spectrometer ◽  
Remote Areas ◽  
Moderate Resolution ◽  
Satellite Sensors ◽  
Resolution Imaging

AbstractSatellite-based Earth observation plays a key role for monitoring volcanoes, especially those which are located in remote areas and which very often are not observed by a terrestrial monitoring network. In our study we jointly analyzed data from thermal (Moderate Resolution Imaging Spectrometer MODIS and Visible Infrared Imaging Radiometer Suite VIIRS), optical (Operational Land Imager and Multispectral Instrument) and synthetic aperture radar (SAR) (Sentinel-1 and TerraSAR-X) satellite sensors to investigate the mid-October 2019 surtseyan eruption at Late’iki Volcano, located on the Tonga Volcanic Arc. During the eruption, the remains of an older volcanic island formed in 1995 collapsed and a new volcanic island, called New Late’iki was formed. After the 12 days long lasting eruption, we observed a rapid change of the island’s shape and size, and an erosion of this newly formed volcanic island, which was reclaimed by the ocean two months after the eruption ceased. This fast erosion of New Late’iki Island is in strong contrast to the over 25 years long survival of the volcanic island formed in 1995.

Intercomparison of Cloud Products based on S5P/TROPOMI Level 1b Data Version 1 and the updated Version 2

2021 ◽  
Author(s):  
Miriam Latsch ◽  
Andreas Richter ◽  
John P. Burrows ◽  
Thomas Wagner ◽  
Holger Sihler ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Oxygen Absorption ◽  
Data Sets ◽  
Absorption Bands ◽  
Data Set ◽  
Trace Species ◽  
Retrieval Scheme

<p>The first European Sentinel satellite for monitoring the composition of the Earth’s atmosphere, the Sentinel 5 Precursor (S5p), carries the TROPOspheric Monitoring Instrument (TROPOMI) to map trace species of the global atmosphere at high spatial resolution. Retrievals of tropospheric trace gas columns from satellite measurements are strongly influenced by clouds. Thus, cloud retrieval algorithms were developed and implemented in the trace gas processing chain to consider this impact.</p><p>In this study, different cloud products available for NO<sub>2</sub> retrievals based on the TROPOMI level 1b data version 1 and an updated TROPOMI level 1b test data set of version 2 (Diagnostic Data Set 2B, DDS2B) are analyzed. The data sets include a) the TROPOMI level 2 OCRA/ROCINN (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks) cloud products CRB (cloud as reflecting boundaries) and CAL (clouds as layers), b) the FRESCO (Fast Retrieval Scheme for Clouds from Oxygen absorption bands) cloud product,  c) the cloud fraction from the NO<sub>2</sub> fitting window, d) the VIIRS (Visible Infrared Imaging Radiometer Suite) cloud product, and e) the MICRU (Mainz Iterative Cloud Retrieval Utilities) cloud fraction. The cloud products are compared with regard to cloud fraction, cloud height, cloud albedo/optical thickness, flagging and quality indicators in all 4 seasons. In particular, the differences of the cloud products under difficult situations such as snow or ice cover and sun glint are investigated.</p><p>We present results of a statistical analysis on a limited data set comparing cloud products from the current and the upcoming lv2 data versions and their approaches. The aim of this study is to better understand TROPOMI cloud products and their quantitative impacts on trace gas retrievals.</p>

Nuclide Composition Benchmark Data Set for Verifying Burnup Codes on Spent Light Water Reactor Fuels

2002 ◽  
Vol 137 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Yoshinori Nakahara ◽  
Kenya Suyama ◽  
Jun Inagawa ◽  
Ryuji Nagaishi ◽  
Setsumi Kurosawa ◽  
...  
Keyword(s):  
Light Water Reactor ◽  
Data Set ◽  
Light Water ◽  
Water Reactor ◽  
Benchmark Data ◽  
Nuclide Composition

scholarly journals Detection and Quantification of Snow Algae with an Airborne Imaging Spectrometer

2001 ◽  
Vol 67 (11) ◽  
pp. 5267-5272 ◽  
Author(s):  
Thomas H. Painter ◽  
Brian Duval ◽  
William H. Thomas ◽  
Maria Mendez ◽  
Sara Heintzelman ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Wavelength Range ◽  
Spectral Reflectance ◽  
Infrared Imaging ◽  
Spectral Signature ◽  
Absorption Feature ◽  
Imaging Spectrometer ◽  
Algal Concentration ◽  
Airborne Imaging ◽  
Spectrometer Data

ABSTRACT We describe spectral reflectance measurements of snow containing the snow alga Chlamydomonas nivalis and a model to retrieve snow algal concentrations from airborne imaging spectrometer data. Because cells of C. nivalis absorb at specific wavelengths in regions indicative of carotenoids (astaxanthin esters, lutein, β-carotene) and chlorophylls a and b, the spectral signature of snow containing C. nivalis is distinct from that of snow without algae. The spectral reflectance of snow containing C. nivalis is separable from that of snow without algae due to carotenoid absorption in the wavelength range from 0.4 to 0.58 μm and chlorophyll a and babsorption in the wavelength range from 0.6 to 0.7 μm. The integral of the scaled chlorophyll a and b absorption feature (I 0.68) varies with algal concentration (Ca ). Using the relationshipCa = 81019.2 I 0.68+ 845.2, we inverted Airborne Visible Infrared Imaging Spectrometer reflectance data collected in the Tioga Pass region of the Sierra Nevada in California to determine algal concentration. For the 5.5-km2 region imaged, the mean algal concentration was 1,306 cells ml−1, the standard deviation was 1,740 cells ml−1, and the coefficient of variation was 1.33. The retrieved spatial distribution was consistent with observations made in the field. From the spatial estimates of algal concentration, we calculated a total imaged algal biomass of 16.55 kg for the 0.495-km2 snow-covered area, which gave an areal biomass concentration of 0.033 g/m2.

Joint ESA and NASA Imaging Spectrometer Airborne Campaign to Support CHIME and SBG

2021 ◽  
Author(s):  
Robert Green ◽  
Michael Rast ◽  
Michael Schaepman ◽  
Andreas Hueni ◽  
Michael Eastwood
Keyword(s):  
Uncertainty Quantification ◽  
Hyperspectral Imaging ◽  
Infrared Imaging ◽  
State Of The Art ◽  
Atmospheric Correction ◽  
Imaging Spectrometer ◽  
Science Collaboration ◽  
Study Sites ◽  
University Of Zurich ◽  
The University

<p>In 2018 a joint ESA and NASA airborne campaign was orchestrated with the University of Zurich to advance cooperation and harmonization of algorithms and products from imaging spectrometer measurements.  This effort was intended to benefit the future candidate European Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology mission. For this campaign, the Airborne Visible/Infrared Imaging Spectrometer Next Generation was deployed from May to July 2018.  Twenty-four study sites were measured across Germany, Italy, and Switzerland.  All measurements were rapidly calibrated, atmospherically corrected, and made available to NASA and ESA investigators.  An expanded 2021 campaign is now planned with goals to: 1) further test and evaluate new state-of-the-art science algorithms: atmospheric correction, etc; 2)  grow international science collaboration in support of ESA CHIME and NASA SBG; 3) test/demonstrate calibration, validation, and uncertainty quantification approaches;  4) collect strategic cross-comparison under flights of space missions: DESIS, PRISMA, Sentinels, etc.  In this paper, we present an overview of the key results from the 2018 campaign and plans for the 2021 campaign.</p><p> </p>

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