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2022 ◽  
Vol 14 (2) ◽  
pp. 383
Author(s):  
Xinxi Feng ◽  
Le Han ◽  
Le Dong

Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels. According to the spatial prior knowledge, there are many regularizations designed to improve the performance of unmixing algorithms, such as the total variation (TV) regularization. However, these methods mostly ignore the similar characteristics among different spectral bands. To solve this problem, this paper proposes a group sparse regularization that uses the weighted constraint of the L2,1 norm, which can not only explore the similar characteristics of the hyperspectral image in the spectral dimension, but also keep the data smooth characteristics in the spatial dimension. In summary, a non-negative tensor factorization framework based on weighted group sparsity constraint is proposed for hyperspectral images. In addition, an effective alternating direction method of multipliers (ADMM) algorithm is used to solve the algorithm proposed in this paper. Compared with the existing popular methods, experiments conducted on three real datasets fully demonstrate the effectiveness and advancement of the proposed method.


Author(s):  
Ram C. Sharma ◽  
Keitarou Hara

This research introduces Genus-Physiognomy-Ecosystem (GPE) mapping at a prefecture level through machine learning of multi-spectral and multi-temporal satellite images at 10m spatial resolution, and later integration of prefecture wise maps into country scale for dealing with 88 GPE types to be classified from a large size of training data involved in the research effectively. This research was made possible by harnessing entire archives of Level-2A product, Bottom of Atmosphere reflectance images collected by MultiSpectral Instruments onboard a constellation of two polar-orbiting Sentinel-2 mission satellites. The satellite images were pre-processed for cloud masking and monthly median composite images consisting of 10 multi-spectral bands and 7 spectral indexes were generated. The ground truth labels were extracted from extant vegetation survey maps by implementing systematic stratified sampling approach and noisy labels were dropped out for preparing a reliable ground truth database. Graphics Processing Unit (GPU) implementation of Gradient Boosting Decision Trees (GBDT) classifier was employed for classification of 88 GPE types from 204 satellite features. The classification accuracy computed with 25% test data varied from 65-81% in terms of F1-score across 48 prefectural regions. This research produced seamless maps of 88 GPE types first time at a country scale with an average 72% F1-score.


2022 ◽  
Vol 14 (1) ◽  
pp. 217
Author(s):  
Bishwas Praveen ◽  
Vineetha Menon

Hyperspectral remote sensing presents a unique big data research paradigm through its rich information captured across hundreds of spectral bands, which embodies vital spatial and temporal information about the underlying land cover. Deep-learning-based hyperspectral data analysis methodologies have made significant advancements over the past few years. Despite their success, most deep learning frameworks for hyperspectral data classification tend to suffer in terms of computational and classification efficacy as the data size increases. This is largely due to their equal emphasis criteria on the rich spectral information present in the data, albeit all of the spectral information not being essential for hyperspectral data analysis. On the contrary, this redundant information present in the spectral bands can deter the performance of hyperspectral data analysis techniques. Therefore, in this work, we propose a novel bidirectional spectral attention mechanism, which is computationally efficient and capable of adaptive spectral information diversification through selective emphasis on spectral bands that comprise more information and suppress the ones with lesser information. The concept of 3D-convolutions in tandem with bidirectional long short-term memory (LSTM) is used in the proposed architecture as spectral attention mechanism. A feedforward neural network (FNN)-based supervised classification is then performed to validate the performance of our proposed approach. Experimental results reveal that the proposed hyperspectral data analysis model with spectral attention mechanism outperforms other spatial- and spectral-information-extraction-based hyperspectral data analysis techniques compared.


2022 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Daniele Locci ◽  
Antonino Petralia ◽  
Giuseppina Micela ◽  
Antonio Maggio ◽  
Angela Ciaravella ◽  
...  

Abstract The interaction of exoplanets with their host stars causes a vast diversity in bulk and atmospheric compositions and physical and chemical conditions. Stellar radiation, especially at the shorter wavelengths, drives the chemistry in the upper atmospheric layers of close orbiting gaseous giants, providing drastic departures from equilibrium. In this study, we aim at unfolding the effects caused by photons in different spectral bands on the atmospheric chemistry. This task is particularly difficult because the characteristics of chemical evolution emerge from many feedbacks on a wide range of timescales, and because of the existing correlations among different portions of the stellar spectrum. In describing the chemistry, we have placed particular emphasis on the molecular synthesis induced by X-rays. The weak X-ray photoabsorption cross sections of the atmospheric constituents boost the gas ionization to pressures inaccessible to vacuum and extreme-ultraviolet photons. Although X-rays interact preferentially with metals, they produce a secondary electron cascade able to ionize efficiently hydrogen- and helium-bearing species, giving rise to a distinctive chemistry.


2022 ◽  
Vol 34 (2) ◽  
pp. 272-278
Author(s):  
Thiyam Samrat Singh ◽  
Thiyam David Singh

Interaction of N-acetyl-L-cysteine (NAC) with Pr3+ (Pr(NO3)3·6H2O) and Nd3+ (Nd(NO3)3·6H2O) ions are studied in presence of Ca2+ (Ca(NO3)3·4H2O) ion in an aqueous and organic solvent by applying the spectroscopic technique for quantitative probe of 4f-4f transition. The complexation was determined by the variation in the intensities of 4f-4f absorption spectral bands and by applying the change of symmetric properties of electronic-dipole known as Judd-Ofelt parameters Tλ (λ = 2,4,6). On the addition of Ca2+ ion in the binary complexation of praseodymium and neodymium with N-acetyl-L-cysteine (NAC) there is an intensification of bands which shows the effect of Ca2+ toward the heterobimetallic complex formation. Other parameters like Slater-Condon (Fk), bonding (b1/2), the Nephelauxetic ratio (β), percentage covalency (δ) are also used to correlate the complexation of metals with N-acetyl-L-cysteine (NAC). With the minor change in coordination around Pr3+ and Nd3+ ions, the sensitivity of 4f-4f bands is detected and further used to explain the coordination of N-acetyl-L-cysteine (NAC) with Pr3+ and Nd3+ in presence of Ca2+. The variation in oscillator strength (Pobs), energy (Eobs) and dipole intensity parameter help in supporting the heterobimetallic complexation of N-acetyl-L-cysteine. In kinetics investigation, the rate of the complexation of both hypersensitive and pseudo-hypersensitive transition is evaluated at various temperature in DMF solvent. The value of the thermodynamic parameters such as ΔHo, ΔSo and ΔGo and activation energy (Ea) also evaluated.


Author(s):  
Arindom Ain

Abstract: Land use and land cover (LULC) provides a way to classify objects on the surface of Earth. This paper aims to identify the varying land cover classes by stacking of 6 spectral bands and 10 different generated indices from those bands together. We have considered the multispectral images of Landsat 7 for our research. It is seen that instead of using only basic spectral bands (blue, green, red, nir, swir1 and swir2) for classification, stacking relevant indices of multiple target classes like ndvi, evi, nbr, BU, etc. with basic bands generates more precise results. In this study, we have used automated clustering techniques for generating 5 different class labels for training the model. These labels are further used to develop a predictive model to classify LULC classes. The proposed classifier is compared with the SVM and KNN classifiers. The results show that this proposed strategy gives preferable outcomes over other techniques. After training the model over 50 epochs, an accuracy of 93.29% is achieved. Keywords: Land use, land cover, CNN, ISODATA, indices


Molecules ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 163
Author(s):  
Giulia Festa ◽  
Claudia Scatigno ◽  
Francesco Armetta ◽  
Maria Luisa Saladino ◽  
Veronica Ciaramitaro ◽  
...  

Spectral preprocessing data and chemometric tools are analytical methods widely applied in several scientific contexts i.e., in archaeometric applications. A systematic classification of natural powdered pigments of organic and inorganic nature through Principal Component Analysis with a multi-instruments spectroscopic study is presented here. The methodology allows the access to elementary and molecular unique benchmarks to guide and speed up the identification of an unknown pigment and its recipe. This study is conducted on a set of 48 powdered pigments and tested on a real-case sample from the wall painting in S. Maria Delle Palate di Tusa (Messina, Italy). Four spectroscopic techniques (X-ray Fluorescence, Raman, Attenuated Total Reflectance and Total Reflectance Infrared Spectroscopies) and six different spectrometers are tested to evaluate the impact of different setups. The novelty of the work is to use a systematic approach on this initial dataset using the entire spectroscopic energy range without any windows selection to solve problems linked with the manipulation of large analytes/materials to find an indistinct property of one or more spectral bands opening new frontiers in the dataset spectroscopic analyses.


Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 13
Author(s):  
Tamara Škorić

The development of smart cars with e-health services allows monitoring of the health condition of the driver. Driver comfort is preserved by the use of capacitive electrodes, but the recorded signal is characterized by large artifacts. This paper proposes a method for reducing artifacts from the ECG signal recorded by capacitive electrodes (cECG) in moving subjects. Two dominant artifact types are coarse and slow-changing artifacts. Slow-changing artifacts removal by classical filtering is not feasible as the spectral bands of artifacts and cECG overlap, mostly in the band from 0.5 to 15 Hz. We developed a method for artifact removal, based on estimating the fluctuation around linear trend, for both artifact types, including a condition for determining the presence of coarse artifacts. The method was validated on cECG recorded while driving, with the artifacts predominantly due to the movements, as well as on cECG recorded while lying, where the movements were performed according to a predefined protocol. The proposed method eliminates 96% to 100% of the coarse artifacts, while the slow-changing artifacts are completely reduced for the recorded cECG signals larger than 0.3 V. The obtained results are in accordance with the opinion of medical experts. The method is intended for reliable extraction of cardiovascular parameters to monitor driver fatigue status.


2021 ◽  
Vol 13 (24) ◽  
pp. 5139
Author(s):  
Aurélie Michel ◽  
Carlos Granero-Belinchon ◽  
Charlène Cassante ◽  
Paul Boitard ◽  
Xavier Briottet ◽  
...  

The monitoring of the Land Surface Temperature (LST) by remote sensing in urban areas is of great interest to study the Surface Urban Heat Island (SUHI) effect. Thus, it is one of the goals of the future spaceborne mission TRISHNA, which will carry a thermal radiometer onboard with four bands at a 60-m spatial resolution, acquiring daytime and nighttime. In this study, TRISHNA-like data are simulated from Airborne Hyperspectral Scanner (AHS) data over the Madrid urban area at 4-m resolution. To retrieve the LST, the Temperature and Emissivity Separation (TES) algorithm is applied with four spectral bands considering two main original approaches compared with the classical TES algorithm. First, calibration and validation datasets with a large number of artificial materials are considered (called urban-oriented database), contrary to most of the previous studies that do not use a large number of artificial material spectra during the calibration step, thus impacting the LST retrieval over these materials. This approach produces one TES algorithm with one empirical relationship, called 1MMD TES. Second, two empirical relationships are used, one for the artificial materials and the other for the natural ones. These relationships are defined thanks to two calibration datasets (artificial-surface-oriented database and natural-surface-oriented database, respectively), one containing mainly artificial materials and the other mainly natural ones. Finally, in order to use two empirical relationships, a ground cover classification map is given to the TES algorithm to separate artificial pixels from natural ones. This approach produces one material-oriented TES algorithm with two empirical relationships, called 2MMD TES. In order to perform a complete comparison of these two addenda in the TES algorithm and their impact on the LST retrieval, both AHS and TRISHNA spatial resolutions are studied, i.e., 4-m and 60-m resolutions, respectively. Relative to the calibration of the TES algorithm, we conclude that (1) the urban-oriented database is more representative of the urban areas than previous databases from the state-of-the-art, and (2) using two databases (artificial-surface-oriented and natural-surface-oriented) instead of one prevents the overestimation of the LST over natural materials and the underestimation over artificial ones. Thus, for both studied spatial resolutions (AHS and TRISHNA), we find that the 2MMD TES outperforms the 1MMD TES. This difference is especially important for artificial materials, corroborating the above conclusion. Furthermore, the comparison with ground measurements shows that, on 4-m spatial resolution images, the 2MMD TES outperforms both the 1MMD TES and the TES from the state-of-the-art used in this study. Finally, we conclude that the 2MMD TES method, with only four spectral bands, better retrieves the LST over artificial and natural materials and that the future TRISHNA sensor is suited for the monitoring of the LST over urban areas and the SUHI effect.


2021 ◽  
Vol 13 (24) ◽  
pp. 5054
Author(s):  
Blessing Kavhu ◽  
Zama Eric Mashimbye ◽  
Linda Luvuno

Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural resources within the Okavango Basin is undoubtedly a complex issue. It is often difficult to accurately map land use and cover using remote sensing in heterogeneous landscapes. This study investigates the combined value of climate-based regionalization and integration of spectral bands with spectral indices to enhance the accuracy of multi-temporal land use/cover classification using deep learning and machine learning approaches. Two experiments were set up, the first entailing the integration of spectral bands with spectral indices and the second involving the combined integration of spectral indices and climate-based regionalization based on Koppen–Geiger climate zones. Landsat 5 TM and Landsat 8 OLI images, machine learning classifiers (random forest and extreme gradient boosting), and deep learning (neural network and deep neural network) classifiers were used in this study. Supervised classification using a total of 5140 samples was conducted for the years 1996, 2004, 2013, and 2020. Average overall accuracy and Kappa coefficients were used to validate the results. The study found that the integration of spectral bands with indices improves the accuracy of land use/cover classification using machine learning and deep learning. Post-feature selection combinations yield higher accuracies in comparison to combinations of bands and indices. A combined integration of spectral indices with bands and climate-based regionalization did not significantly improve the accuracy of land use/cover classification consistently for all the classifiers (p < 0.05). However, post-feature selection combinations and climate-based regionalization significantly improved the accuracy for all classifiers investigated in this study. Findings of this study will improve the reliability of land use/cover monitoring in complex heterogeneous TDBs.


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