scholarly journals AT2ES: Simultaneous Atmospheric Transmittance-Temperature-Emissivity Separation Using Online Upper Midwave Infrared Hyperspectral Images

2021 ◽  
Vol 13 (7) ◽  
pp. 1249
Author(s):  
Sungho Kim ◽  
Jungsub Shin ◽  
Sunho Kim
Keyword(s):  
Air Temperature ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Co2 Absorption ◽  
Infrared Band ◽  
Data Regression ◽  
Midwave Infrared ◽  
Novel Method ◽  
Atmospheric Transmittance

This paper presents a novel method for atmospheric transmittance-temperature-emissivity separation (AT2ES) using online midwave infrared hyperspectral images. Conventionally, temperature and emissivity separation (TES) is a well-known problem in the remote sensing domain. However, previous approaches use the atmospheric correction process before TES using MODTRAN in the long wave infrared band. Simultaneous online atmospheric transmittance-temperature-emissivity separation starts with approximation of the radiative transfer equation in the upper midwave infrared band. The highest atmospheric band is used to estimate surface temperature, assuming high emissive materials. The lowest atmospheric band (CO2 absorption band) is used to estimate air temperature. Through onsite hyperspectral data regression, atmospheric transmittance is obtained from the y-intercept, and emissivity is separated using the observed radiance, the separated object temperature, the air temperature, and atmospheric transmittance. The advantage with the proposed method is from being the first attempt at simultaneous AT2ES and online separation without any prior knowledge and pre-processing. Midwave Fourier transform infrared (FTIR)-based outdoor experimental results validate the feasibility of the proposed AT2ES method.

scholarly journals What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

2020 ◽  
Vol 12 (24) ◽  
pp. 4077
Author(s):  
Michał Krupiński ◽  
Anna Wawrzaszek ◽  
Wojciech Drzewiecki ◽  
Małgorzata Jenerowicz ◽  
Sebastian Aleksandrowicz
Keyword(s):  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
High Spectral Resolution ◽  
Absorption Bands ◽  
Landscape Type ◽  
Spectral Bands ◽  
Before And After ◽  
Additional Value

Hyperspectral images provide complex information about the Earth’s surface due to their very high spectral resolution (hundreds of spectral bands per pixel). Effective processing of such a large amount of data requires dedicated analysis methods. Therefore, this research applies, for the first time, the degree of multifractality to the global description of all spectral bands of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Subsets of four hyperspectral images, presenting four landscape types, are analysed. In particular, we verify whether multifractality can be detected in all spectral bands. Furthermore, we analyse variability in multifractality as a function of wavelength, for data before and after atmospheric correction. We try to identify absorption bands and discuss whether multifractal parameters provide additional value or can help in the problem of dimensionality reduction in hyperspectral data or landscape type classification.

A Semi-Autonomous Method to Detect Cosmic Rays in Raman Hyperspectral Data Sets

2019 ◽  
Vol 73 (9) ◽  
pp. 1019-1027 ◽  
Author(s):  
Kyle Uckert ◽  
Rohit Bhartia ◽  
John Michel
Keyword(s):  
Cosmic Rays ◽  
Cosmic Ray ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Data Sets ◽  
Baseline Drift ◽  
Novel Method ◽  
Deep Ultraviolet ◽  
Astronomical Images ◽  
Spectral Variance

Cosmic rays can degrade Raman hyperspectral images by introducing high-intensity noise to spectra, obfuscating the results of downstream analyses. We describe a novel method to detect cosmic rays in deep ultraviolet Raman hyperspectral data sets adapted from existing cosmic ray removal methods applied to astronomical images. This method identifies cosmic rays as outliers in the distribution of intensity values in each wavelength channel. In some cases, this algorithm fails to identify cosmic rays in data sets with high inter-spectral variance, uncorrected baseline drift, or few spectra. However, this method effectively identifies cosmic rays in spatially uncorrelated hyperspectral data sets more effectively than other cosmic ray rejection methods and can potentially be employed in commercial and robotic Raman systems to identify cosmic rays semi-autonomously.

scholarly journals ADVANCED SURFACE-REFLECTED RADIANCE CORRECTION FOR AIRBORNE HYPERSPECTRAL IMAGERY IN COASTAL RED TIDE DETECTION

2021 ◽  
Vol V-3-2021 ◽  
pp. 73-80
Author(s):  
J.-E. Min ◽  
S.-K. Lee ◽  
J.-H. Ryu
Keyword(s):  
Remote Sensing ◽  
Red Tide ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Coastal Hazards ◽  
Optical Data ◽  
Ratio Analysis ◽  
Red Tides

Abstract. Red tides are among the most common coastal hazards, causing serious damage to the coastal environment. Many satellite sensors can detect red tide blooms, but are limited in their detection of the exact area of the bloom and biological abundance in terms of spatial and spectral resolution. The high spatial and spectral resolutions of hyperspectral airborne remote sensing data may help overcome these limitations to analyze red tide blooms more effectively. To identify potential applications of hyperspectral airborne data in red tide detection, an integrated field campaign was performed in September 2016 off the coast of Tongyeong, South Korea. An AisaEAGLE sensor was installed on a Cessna 208B crewed aircraft to obtain hyperspectral images of an 18 km × 18 km coastal area. To assess the atmospheric correction of the hyperspectral data, in situ optical data and water samples were measured on two vessels concurrent with the flight path. Advanced surface-reflected radiance (Lr) correction and basic atmospheric path radiance (La) correction were performed on the hyperspectral images. Of these, Lr correction comprised a large proportion of the atmospheric correction. The atmosphere-corrected remote sensing reflectance data of the hyperspectral images closely matched the in-situ measurements. The data were assessed for red tide events using ratio analysis and the fluorescence line height technique; the ratio analysis more effectively detected regions with suspected red tides.

scholarly journals The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters

2015 ◽  
Vol 8 (3) ◽  
pp. 1593-1604 ◽  
Author(s):  
C. Bassani ◽  
C. Manzo ◽  
F. Braga ◽  
M. Bresciani ◽  
C. Giardino ◽  
...  
Keyword(s):  
Accuracy Assessment ◽  
Atmospheric Correction ◽  
Solar Spectrum ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Quality Analysis ◽  
Microphysical Properties ◽  
Quantitative Remote Sensing ◽  
The Impact ◽  
Aerosol Types

Abstract. Hyperspectral imaging provides quantitative remote sensing of ocean colour by the high spectral resolution of the water features. The HICO™ (Hyperspectral Imager for the Coastal Ocean) is suitable for coastal studies and monitoring. The accurate retrieval of hyperspectral water-leaving reflectance from HICO™ data is still a challenge. The aim of this work is to retrieve the water-leaving reflectance from HICO™ data with a physically based algorithm, using the local microphysical properties of the aerosol in order to overcome the limitations of the standard aerosol types commonly used in atmospheric correction processing. The water-leaving reflectance was obtained using the HICO@CRI (HICO ATmospherically Corrected Reflectance Imagery) atmospheric correction algorithm by adapting the vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) radiative transfer code. The HICO@CRI algorithm was applied on to six HICO™ images acquired in the northern Mediterranean basin, using the microphysical properties measured by the Acqua Alta Oceanographic Tower (AAOT) AERONET site. The HICO@CRI results obtained with AERONET products were validated with in situ measurements showing an accuracy expressed by r2 = 0.98. Additional runs of HICO@CRI on the six images were performed using maritime, continental and urban standard aerosol types to perform the accuracy assessment when standard aerosol types implemented in 6SV are used. The results highlight that the microphysical properties of the aerosol improve the accuracy of the atmospheric correction compared to standard aerosol types. The normalized root mean square (NRMSE) and the similar spectral value (SSV) of the water-leaving reflectance show reduced accuracy in atmospheric correction results when there is an increase in aerosol loading. This is mainly when the standard aerosol type used is characterized with different optical properties compared to the local aerosol. The results suggest that if a water quality analysis is needed the microphysical properties of the aerosol need to be taken into consideration in the atmospheric correction of hyperspectral data over coastal environments, because aerosols influence the accuracy of the retrieved water-leaving reflectance.

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

scholarly journals Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2649
Author(s):  
Hafiz Ali Imran ◽  
Damiano Gianelle ◽  
Michele Scotton ◽  
Duccio Rocchini ◽  
Michele Dalponte ◽  
...  
Keyword(s):  
Species Richness ◽  
Well Being ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Grassland Species ◽  
Biodiversity Indices ◽  
Grassland Ecosystems ◽  
Natural Grasslands ◽  
Α Diversity ◽  
Diversity Metrics

Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity.

scholarly journals Hyperspectral monitoring of fructose content in marzipan

2018 ◽  
Vol 14 (s1) ◽  
pp. 79-88
Author(s):  
Katalin Badak-Kerti ◽  
Szabina Németh ◽  
Andreas Zitek ◽  
Ferenc Firtha
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Spectral Properties ◽  
Oil Content ◽  
Sugar Content ◽  
Principal Component ◽  
Reducing Sugar ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Analysis Technique

In our research marzipan samples of different sugar to almond paste ratios (1:1, 2:1, 3:1) were stored at 17 °C. Reducing sugar content was measured by analytical method, texture analysis was done by penetrometry, electric characteristics were measured by conductometry and hyperspectral images were taken 6–8 times during the 16 days of storage. For statistical analyses (discriminant analysis, principal component analysis) SPSS program was used. According to our findings with the hyperspectral analysis technique, it is possible to identify how long the samples were stored (after production), and to which class (ratio of sugar to almond) the sample belonged. The main wavelengths which gave the best discrimination results among the days of storage were between 960 and 1100 nm. The type of the marzipan was easy to distinguish with the hyperspectral data; the biggest differences were observed at 1200 and 1400 nm, which are connected to the first overtone of C-H bound, therefore correlate with the oil content. The spatial distribution of penetrometric, electric and spectral properties were also characteristic to fructose content. The fructose content of marzipan is difficult to measure by usual optical ways (polarimetry, spectroscopy), but since fructose is hygroscopic, the spatial distribution of spectral properties can be characteristic.

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