Comparative study on atmospheric correction methods of visible and near-infrared hyperspectral image

Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

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The Quick Atmospheric Correction (QUAC) Algorithm for Hyperspectral Image Processing: Extending QUAC to a Coastal Scene

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) ◽

10.1109/dicta.2015.7371314 ◽

2015 ◽

Cited By ~ 4

Author(s):

S. B. Carr ◽

L. S. Bernstein ◽

S. M. Adler-Golden

Keyword(s):

Image Processing ◽

Hyperspectral Image ◽

Atmospheric Correction ◽

Hyperspectral Image Processing

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A reliable tool based on near-infrared spectroscopy for the monitoring of moisture content in roasted and ground coffee: a comparative study with thermogravimetric analysis

Food Control ◽

10.1016/j.foodcont.2021.108312 ◽

2021 ◽

pp. 108312

Author(s):

Alessio Tugnolo ◽

Valentina Giovenzana ◽

Cristina Malegori ◽

Paolo Oliveri ◽

Andrea Casson ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Thermogravimetric Analysis ◽

Moisture Content ◽

Comparative Study ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Reliable Tool ◽

Ground Coffee

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BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-7-3-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 3-8 ◽

Cited By ~ 2

Author(s):

M. Matsuoka ◽

M. Takagi ◽

S. Akatsuka ◽

R. Honda ◽

A. Nonomura ◽

...

Keyword(s):

Surface Properties ◽

Land Surface ◽

Goodness Of Fit ◽

Near Infrared ◽

Temporal Frequency ◽

Atmospheric Correction ◽

Time Of Day ◽

Coefficient Of Determination ◽

Near Infrared Reflectance ◽

Bidirectional Reflectance

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

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Improvement of Atmospheric Correction of Satellite Sentinel-3/OLCI Data for Oceanic Waters in Presence of Sargassum

Remote Sensing ◽

10.3390/rs14020386 ◽

2022 ◽

Vol 14 (2) ◽

pp. 386

Author(s):

Léa Schamberger ◽

Audrey Minghelli ◽

Malik Chami ◽

François Steinmetz

Keyword(s):

Near Infrared ◽

Ocean Color ◽

Atmospheric Correction ◽

Correction Algorithm ◽

Infrared Band ◽

Economic Issues ◽

Optical Signature ◽

Color Data ◽

The Caribbean ◽

Satellite Ocean Color

The invasive species of brown algae Sargassum gathers in large aggregations in the Caribbean Sea, and has done so especially over the last decade. These aggregations wash up on shores and decompose, leading to many socio-economic issues for the population and the coastal ecosystem. Satellite ocean color data sensors such as Sentinel-3/OLCI can be used to detect the presence of Sargassum and estimate its fractional coverage and biomass. The derivation of Sargassum presence and abundance from satellite ocean color data first requires atmospheric correction; however, the atmospheric correction procedure that is commonly used for oceanic waters needs to be adapted when dealing with the occurrence of Sargassum because the non-zero water reflectance in the near infrared band induced by Sargassum optical signature could lead to Sargassum being wrongly identified as aerosols. In this study, this difficulty is overcome by interpolating aerosol and sunglint reflectance between nearby Sargassum-free pixels. The proposed method relies on the local homogeneity of the aerosol reflectance between Sargassum and Sargassum-free areas. The performance of the adapted atmospheric correction algorithm over Sargassum areas is evaluated. The proposed method is demonstrated to result in more plausible aerosol and sunglint reflectances. A reduction of between 75% and 88% of pixels showing a negative water reflectance above 600 nm were noticed after the correction of the several images.

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Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

Journal of Spectral Imaging ◽

10.1255/jsi.2019.a4 ◽

2019 ◽

Cited By ~ 3

Author(s):

Aoife Gowen ◽

Jun-Li Xu ◽

Ana Herrero-Langreo

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Spatial Information ◽

Hyperspectral Image ◽

Imaging Spectroscopy ◽

Model Performance ◽

Data Sampling ◽

Classification Models ◽

Imaging Data ◽

Selection Of

Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection.

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Visualizing Near Infrared Hyperspectral Images with Generative Adversarial Networks

Remote Sensing ◽

10.3390/rs12233848 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3848

Author(s):

Rongxin Tang ◽

Hualin Liu ◽

Jingbo Wei

Keyword(s):

Near Infrared ◽

Hyperspectral Image ◽

Geographic Location ◽

Hyperspectral Images ◽

Generative Adversarial Networks ◽

Landsat 8 ◽

Multispectral Images ◽

Adversarial Network ◽

Adversarial Networks ◽

Visualization Algorithms

The visualization of near infrared hyperspectral images is valuable for quick view and information survey, whereas methods using band selection or dimension reduction fail to produce good colors as reasonable as corresponding multispectral images. In this paper, an end-to-end neural network of hyperspectral visualization is proposed, based on the convolutional neural networks, to transform a hyperspectral image of hundreds of near infrared bands to a three-band image. Supervised learning is used to train the network where multispectral images are targeted to reconstruct naturally looking images. Each pair of the training images shares the same geographic location and similar moments. The generative adversarial framework is used with an adversarial network to improve the training of the generating network. In the experimental procedure, the proposed method is tested for the near infrared bands of EO-1 Hyperion images with LandSat-8 images as the benchmark, which is compared with five state-of-the-art visualization algorithms. The experimental results show that the proposed method performs better in producing naturally looking details and colors for near infrared hyperspectral images.

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Retrieval and Validation of Water Turbidity at Metre-Scale Using Pléiades Satellite Data: A Case Study in the Gironde Estuary

Remote Sensing ◽

10.3390/rs12060946 ◽

2020 ◽

Vol 12 (6) ◽

pp. 946 ◽

Cited By ~ 3

Author(s):

Yafei Luo ◽

David Doxaran ◽

Quinten Vanhellemont

Keyword(s):

Spatial Variability ◽

Satellite Data ◽

Near Infrared ◽

Atmospheric Correction ◽

Field Measurements ◽

Quality Parameters ◽

Satellite Constellations ◽

Water Turbidity ◽

Gironde Estuary ◽

The Difference

This study investigated the use of frequent metre-scale resolution Pléiades satellite imagery to monitor water quality parameters in the highly turbid Gironde Estuary (GE, SW France). Pléiades satellite data were processed and analyzed in two representative test sites of the GE: 1) the maximum turbidity zone and 2) the mouth of the estuary. The main objectives of this study were to: (i) validate the Dark Spectrum Fitting (DSF) atmospheric correction developed by Vanhellemont and Ruddick (2018) applied to Pléiades satellite data recorded over the GE; (ii) highlight the benefits of frequent metre-scale Pléiades observations in highly turbid estuaries by comparing them to previously validated satellite observations made at medium (250/300 m for MODIS, MERIS, OLCI data) and high (20/30 m for SPOT, OLI and MSI data) spatial resolutions. The results show that the DSF allows for an accurate retrieval of water turbidity by inversion of the water reflectance in the near-infrared (NIR) and red wavebands. The difference between Pléiades-derived turbidity and field measurements was proven to be in the order of 10%. To evaluate the spatial variability of water turbidity at metre scale, Pléiades data at 2 m resolution were resampled to 20 m and 250 m to simulate typical coarser resolution sensors. On average, the derived spatial variability in the GE is lower than or equal to 10% and 26%, respectively, in 20-m and 250-m aggregated pixels. Pléiades products not only show, in great detail, the turbidity features in the estuary and river plume, they also allow to map the turbidity inside ports and capture the complex spatial variations of turbidity along the shores of the estuary. Furthermore, the daily acquisition capabilities may provide additional advantages over other satellite constellations when monitoring highly dynamic estuarine systems.

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