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Journal articles on the topic 'Hyper-(multi-)spectral'

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Published: 7 July 2024

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1

Pande, H., Poonam S. Tiwari, and Shashi Dobhal. "Analyzing hyper-spectral and multi-spectral data fusion in spectral domain." Journal of the Indian Society of Remote Sensing 37, no. 3 (September 2009): 395–408. http://dx.doi.org/10.1007/s12524-009-0038-2.

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Zhu, Siqi, Kang Su, Migao Li, Zhenqiang Chen, Hao Yin, and Zhen Li. "Multi-type hyper-spectral microscopic imaging system." Optik 127, no. 18 (September 2016): 7218–24. http://dx.doi.org/10.1016/j.ijleo.2016.05.053.

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3

Daigo, M., A. Ono†, R. Urabe‡, and N. Fujiwara. "Pattern decomposition method for hyper-multi-spectral data analysis." International Journal of Remote Sensing 25, no. 6 (March 2004): 1153–66. http://dx.doi.org/10.1080/0143116031000139872.

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4

Chatoux, Hermine, Noël Richard, and Bruno Mercier. "Colour key-point detection." London Imaging Meeting 2020, no. 1 (September 29, 2020): 114–18. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-02.

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A lot of image processing tasks require key-point detection. If grey-level approach are numerous, colour and hyper-spectral ones are scarce. In this paper, we propose a generic key-point detection for colour, multi and hyper-spectral images. A new synthetic database is created to compare key-point detection approaches. Our method improves detection when the image complexity increases.
5

Yoshikawa, H., M. Murahashi, M. Saito, S. Jiang, M. Iga, and E. Tamiya. "Parallelized label-free detection of protein interactions using a hyper-spectral imaging system." Analytical Methods 7, no. 12 (2015): 5157–61. http://dx.doi.org/10.1039/c5ay00738k.

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6

Awad, Mohamad M., and Marco Lauteri. "Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests." Sustainability 13, no. 10 (May 16, 2021): 5548. http://dx.doi.org/10.3390/su13105548.

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Forest-type classification is a very complex and difficult subject. The complexity increases with urban and peri-urban forests because of the variety of features that exist in remote sensing images. The success of forest management that includes forest preservation depends strongly on the accuracy of forest-type classification. Several classification methods are used to map urban and peri-urban forests and to identify healthy and non-healthy ones. Some of these methods have shown success in the classification of forests where others failed. The successful methods used specific remote sensing data technology, such as hyper-spectral and very high spatial resolution (VHR) images. However, both VHR and hyper-spectral sensors are very expensive, and hyper-spectral sensors are not widely available on satellite platforms, unlike multi-spectral sensors. Moreover, aerial images are limited in use, very expensive, and hard to arrange and manage. To solve the aforementioned problems, an advanced method, self-organizing–deep learning (SO-UNet), was created to classify forests in the urban and peri-urban environment using multi-spectral, multi-temporal, and medium spatial resolution Sentinel-2 images. SO-UNet is a combination of two different machine learning technologies: artificial neural network unsupervised self-organizing maps and deep learning UNet. Many experiments have been conducted, and the results showed that SO-UNet overwhelms UNet significantly. The experiments encompassed different settings for the parameters that control the algorithms.
7

Mancini, Adriano, Emanuele Frontoni, and Primo Zingaretti. "Challenges of multi/hyper spectral images in precision agriculture applications." IOP Conference Series: Earth and Environmental Science 275 (May 21, 2019): 012001. http://dx.doi.org/10.1088/1755-1315/275/1/012001.

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Sun, Li-wei, Xin Ye, Wei Fang, Zhen-lei He, Xiao-long Yi, and Yu-peng Wang. "Radiometric calibration of hyper-spectral imaging spectrometer based on optimizing multi-spectral band selection." Optoelectronics Letters 13, no. 6 (November 2017): 405–8. http://dx.doi.org/10.1007/s11801-017-7174-7.

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9

Li, Jin, and Zilong Liu. "Compression of hyper-spectral images using an accelerated nonnegative tensor decomposition." Open Physics 15, no. 1 (December 29, 2017): 992–96. http://dx.doi.org/10.1515/phys-2017-0123.

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AbstractNonnegative tensor Tucker decomposition (NTD) in a transform domain (e.g., 2D-DWT, etc) has been used in the compression of hyper-spectral images because it can remove redundancies between spectrum bands and also exploit spatial correlations of each band. However, the use of a NTD has a very high computational cost. In this paper, we propose a low complexity NTD-based compression method of hyper-spectral images. This method is based on a pair-wise multilevel grouping approach for the NTD to overcome its high computational cost. The proposed method has a low complexity under a slight decrease of the coding performance compared to conventional NTD. We experimentally confirm this method, which indicates that this method has the less processing time and keeps a better coding performance than the case that the NTD is not used. The proposed approach has a potential application in the loss compression of hyper-spectral or multi-spectral images
10

Archambault, L., F. Theriault Proulx, S. Beddar, and L. Beaulieu. "PO-0807 FORMALISM FOR HYPER-SPECTRAL, MULTI-POINT, PLASTIC SCINTILLATION DETECTORS." Radiotherapy and Oncology 103 (May 2012): S313—S314. http://dx.doi.org/10.1016/s0167-8140(12)71140-4.

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11

Zhang, Canting, Xicun Zhu, Meixuan Li, Yuliang Xue, Anran Qin, Guining Gao, Mengxia Wang, and Yuanmao Jiang. "Utilization of the Fusion of Ground-Space Remote Sensing Data for Canopy Nitrogen Content Inversion in Apple Orchards." Horticulturae 9, no. 10 (September 29, 2023): 1085. http://dx.doi.org/10.3390/horticulturae9101085.

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Utilizing multi-source remote sensing data fusion to achieve efficient and accurate monitoring of crop nitrogen content is crucial for precise crop management. In this study, an effective integrated method for inverting nitrogen content in apple orchard canopies was proposed based on the fusion of ground-space remote sensing data. Firstly, ground hyper-spectral data, unmanned aerial vehicles (UAVs) multi-spectral data, and apple leaf samples were collected from the apple tree canopy. Secondly, the canopy spectral information was extracted, and the hyper-spectral and UAV multi-spectral data were fused using the Convolution Calculation of the Spectral Response Function (SRF-CC). Based on the raw and simulated data, the spectral feature parameters were constructed and screened, and the canopy abundance parameters were constructed using simulated multi-spectral data. Thirdly, a variety of machine-learning models were constructed and verified to identify the optimal inversion model for spatially inverting the canopy nitrogen content (CNC) in apple orchards. The results demonstrated that SRF-CC was an effective method for the fusion of ground-space remote sensing data, and the fitting degree (R2) of raw and simulated data in all bands was higher than 0.70; the absolute values of the correlation coefficients (|R|) between each spectral index and the CNC increased to 0.55–0.68 after data fusion. The XGBoost model established based on the simulated data and canopy abundance parameters was the optimal model for the CNC inversion (R2 = 0.759, RMSE = 0.098, RPD = 1.855), and the distribution of the CNC obtained from the inversion was more consistent with the actual distribution. The findings of this study can provide the theoretical basis and technical support for efficient and non-destructive monitoring of canopy nutrient status in apple orchards.
12

ZHANG, Yuanfei, Zian YANG, Pubin ZHANG, Feifei SHI, and Jianguo ZHANG. "Research on Background-Anomaly Sub-space Model of Hyper (Multi-) spectral Data." Geo-information Science 11, no. 3 (June 25, 2010): 282–91. http://dx.doi.org/10.3724/sp.j.1047.2009.00282.

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13

TANG Yan-hui, 唐艳慧, 赵鹏 ZHAO Peng, and 王承琨 WANG Cheng-kun. "Texture classification algorithm of wood hyper-spectral image based on multi-fractal spectra." Chinese Journal of Liquid Crystals and Displays 34, no. 12 (2019): 1182–90. http://dx.doi.org/10.3788/yjyxs20193412.1182.

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14

Esposito, Alessandro, Simon Schlachter, Arjen N. Bader, Lucio Pancheri, David Stoppa, Ashok R. Venkitaraman, Clemens F. Kaminski, and Hans C. Gerritsen. "Multiplexed Measurement of Molecular Interactions using Hyper-Spectral Imaging and Multi-Parametric Detection." Biophysical Journal 98, no. 3 (January 2010): 761a. http://dx.doi.org/10.1016/j.bpj.2009.12.4179.

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15

Dyson, Jack, Adriano Mancini, Emanuele Frontoni, and Primo Zingaretti. "Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data." Remote Sensing 11, no. 16 (August 9, 2019): 1859. http://dx.doi.org/10.3390/rs11161859.

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One of the most challenging problems in precision agriculture is to correctly identify and separate crops from the soil. Current precision farming algorithms based on artificially intelligent networks use multi-spectral or hyper-spectral data to derive radiometric indices that guide the operational management of agricultural complexes. Deep learning applications using these big data require sensitive filtering of raw data to effectively drive their hidden layer neural network architectures. Threshold techniques based on the normalized difference vegetation index (NDVI) or other similar metrics are generally used to simplify the development and training of deep learning neural networks. They have the advantage of being natural transformations of hyper-spectral or multi-spectral images that filter the data stream into a neural network, while reducing training requirements and increasing system classification performance. In this paper, to calculate a detailed crop/soil segmentation based on high resolution Digital Surface Model (DSM) data, we propose the redefinition of the radiometric index using a directional mathematical filter. To further refine the analysis, we feed this new radiometric index image of about 3500 × 4500 pixels into a relatively small Convolution Neural Network (CNN) designed for general image pattern recognition at 28 × 28 pixels to evaluate and resolve the vegetation correctly. We show that the result of applying a DSM filter to the NDVI radiometric index before feeding it into a Convolutional Neural Network can potentially improve crop separation hit rate by 65%.
16

Moshou, D., C. Bravo, R. Oberti, J. West, L. Bodria, A. McCartney, and H. Ramon. "Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps." Real-Time Imaging 11, no. 2 (April 2005): 75–83. http://dx.doi.org/10.1016/j.rti.2005.03.003.

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17

Aiazzi, B., P. Alba, L. Alparone, and S. Baronti. "Lossless compression of multi/hyper-spectral imagery based on a 3-D fuzzy prediction." IEEE Transactions on Geoscience and Remote Sensing 37, no. 5 (1999): 2287–94. http://dx.doi.org/10.1109/36.789625.

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18

Imanian, A., M. H. Tangestani, and A. Asadi. "INVESTIGATION OF SPECTRAL CHARACTERISTICS OF CARBONATE ROCKS – A CASE STUDY ON POSHT MOLEH MOUNT IN IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 553–57. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-553-2019.

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Abstract. Recent developments in the image processing approaches and the availability of multi and/or hyper spectral remote sensing data with high spectral, spatial and temporal resolutions have made remote sensing technique of great interest in investigations of geological sciences. One of the biggest advantage of the application of remote sensing in geology is recognizing the type of unknown rocks and minerals. In this study, an investigation on spectral features of carbonate rocks (i.e. calcite, dolomite, and dolomitized calcite) were done in terms of main absorptions, the reasons of those absorptions and comparison of these absorption with Johns Hopkins University (JHU) spectral library and laboratory spectra of Analytical Spectral Devices (ASD) instrument. For this purpose, we used the VNIR and SWIR bands of ASTER and OLI datasets. Finally, we applied the Spectral Analyst Algorithm in order to comparison between the obtained spectra from ASTER dataset and carbonate spectra of JHU spectral library.
19

Mr. B. Naga Rajesh. "Effective Morphological Transformation and Sub-pixel Classification of Clustered Images." International Journal of New Practices in Management and Engineering 8, no. 01 (March 31, 2019): 08–14. http://dx.doi.org/10.17762/ijnpme.v8i01.74.

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The main aim of this research work is to perform the morphological operations with reduced time complexity and area complexity. Morphological operation is the key element in any image processing. Finding the maximum and minimum using a window of defined size will imply to the morphological dilation and erosion respectively. So the proposed algorithm should be fast in the comparison and sorting, this way the time complexity could be reduced. It’s believed that the anchor concept will fetch this cause. The idea behind this is it fixes a pixel and setting it as the center pixel all the surrounding pixels will be processed. Moreover this is now been implemented for rectangular structuring element. This paper attempts the same for flat and 3D structuring elements. Hyper-spectral Imaging is a developing zone of remote detecting applications. Hyper-spectral pictures incorporate more extravagant and better otherworldly data than the multi-spectral pictures got previously. Hyper-otherworldly pictures are described by an exchange off between the unearthly and spatial resolution. The principle issue of the hyper-ghostly information is the generally low spatial goal. For arrangement, the serious issue brought about by low spatial goal is the blended pixels. Blended pixels alluded to the pixels which are involved by more than one land spread class. In the proposed procedure another strategy is utilized to address the issue of blended pixels and to get a better spatial goal of the land spread characterization maps. The strategy misuses the upsides of both picture bunching methods and phantom dimming calculations, so as to decide the fragmentary plenitudes of the classes at a sub-pixel scale. Spatial regularization by Flank planning method is at last performed to spatially find the got classes at sub-pixel level.
20

Mvodo Martin Paulin, Zanga, Koko Same Louis Christian, and Essiben Dikoundou Jean-François. "Linear precoder optimization of spectral efficiency of time division duplex hyper MIMO system with pilot contamination." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (March 1, 2023): 1520. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1520-1528.

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Our work is developed in context of studing Massive MIMO in a 5G context. The aim is to optimize spectral efficiency of several users hyper MIMO system during Uplink communication in a multi-cell contaminated pilot environment, using a new type of precoders called single cell-minimum mean square eroor (S-MMSE) and multicell-minimum mean square eroor (MMMSE). Indeed, we address two key and well-known issues of massive multiuser MIMO (MU-MIMO) environments in a test-driven development (TDD) operation scheme, namely acquisition of uplink channel state information (UL) and optimisation of the bit stream per unit frequency, the spectral efficiency (SE). From a practical point of view, these two notions are inclusively linked. Indeed, a very good channel estimation leads to a better spectral efficiency. In our approcah, we derive from the minimum mean square error estimator (MMSE) to two new types of precoders that can operate in a multicell environment with a contaminated pilot sequence, namely the SMMSE and the M-MMSE. A comparative study performance of these classical precoders such as regulated zero forcing (RZF), ZF (Zero Forcing) and MR (Minimum Ratio) encountered in multi-antenna processing shows an improvement of nearly 51% in terms of system gain and spectral efficiency.
21

Zhao, Xin, and Xing Li. "Band Selection Oriented to Easy-Confused Objects for Classification of Hyperspectral Imagery." Key Engineering Materials 500 (January 2012): 355–61. http://dx.doi.org/10.4028/www.scientific.net/kem.500.355.

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As a dimensionality reduction technique, band selection is an importance pre-processing step for classifiers. In this paper, a band selection approach oriented to easy-confused objects for classification of hyper spectral imagery is presented. Firstly, an Objects Confusion Index (OCI) is established to ascertain the easy-confused objects. Then the two band selection schemes, that are two-class mode and multi-class mode, are designed by adopting Bhattacharyya distance as class reparability measure.
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Marchal, Antoine, Marc-Antoine Miville-Deschênes, François Orieux, Nicolas Gac, Charles Soussen, Marie-Jeanne Lesot, Adrien Revault d’Allonnes, and Quentin Salomé. "ROHSA: Regularized Optimization for Hyper-Spectral Analysis." Astronomy & Astrophysics 626 (June 2019): A101. http://dx.doi.org/10.1051/0004-6361/201935335.

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Context. Extracting the multiphase structure of the neutral interstellar medium is key to understanding star formation in galaxies. The radiative condensation of the diffuse warm neutral medium producing a thermally unstable lukewarm medium and a dense cold medium is closely related to the initial step leading the atomic-to-molecular (HI-to-H2) transition and the formation of molecular clouds. Up to now, the mapping of these phases out of 21 cm emission hyper-spectral cubes has remained elusive mostly due to the velocity blending of individual cold structures present on a given line of sight. As a result, most of the current knowledge about the HI phases rests on a small number of absorption measurements on lines of sight crossing radio sources. Aims. The goal of this work is to develop a new algorithm to perform separation of diffuse sources in hyper-spectral data. Specifically the algorithm was designed in order to address the velocity blending problem by taking advantage of the spatial coherence of the individual sources. The main scientific driver of this effort was to extract the multiphase structure of the HI from 21 cm line emission only, providing a means to map each phase separately, but the algorithm developed here should be generic enough to extract diffuse structures in any hyper-spectral cube. Methods. We developed a new Gaussian decomposition algorithm named ROHSA based on a multi-resolution process from coarse to fine grid. ROHSA uses a regularized nonlinear least-square criterion to take into account the spatial coherence of the emission and the multiphase nature of the gas simultaneously. In order to obtain a solution with spatially smooth parameters, the optimization is performed on the whole data cube at once. The performances of ROHSA were tested on a synthetic observation computed from numerical simulations of thermally bi-stable turbulence. We apply ROHSA to a 21 cm observation of a region of high Galactic latitude from the GHIGLS survey and present our findings. Results. The evaluation of ROHSA on synthetic 21 cm observations shows that it is able to recover the multiphase nature of the HI. For each phase, the power spectra of the column density and centroid velocity are well recovered. More generally, this test reveals that a Gaussian decomposition of HI emission is able to recover physically meaningful information about the underlying three-dimensional fields (density, velocity, and temperature). The application on a real 21 cm observation of a field of high Galactic latitude produces a picture of the multiphase HI, with isolated, filamentary, and narrow (σ ~ 1−2 km s−1) structures, and broader (σ ~ 4−10 km s−1), diffuse, and space-filling components. The test-case field used here contains significant intermediate-velocity clouds that were well mapped out by the algorithm. As ROHSA is designed to extract spatially coherent components, it performs well at projecting out the noise. Conclusions. In this paper we introduce ROHSA, a new algorithm that performs a separation of diffuse sources in hyper-spectral data on the basis of a Gaussian decomposition. The algorithm makes no assumption about the nature of the sources, except that each one has a similar line width. The tests we made shows that ROHSA is well suited to decomposing complex 21 cm line emission of regions of high Galactic latitude, but its design is general enough that it could be applied to any hyper-spectral data type for which a Gaussian model is relevant.
23

El-Habashi, Ahmed, Jeffrey Bowles, Robert Foster, Deric Gray, and Malik Chami. "Polarized observations for advanced atmosphere-ocean algorithms using airborne multi-spectral hyper-angular polarimetric imager." Journal of Quantitative Spectroscopy and Radiative Transfer 262 (March 2021): 107515. http://dx.doi.org/10.1016/j.jqsrt.2021.107515.

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24

Borfecchia, Flavio, Carla Micheli, Luigi De Cecco, Gianmaria Sannino, Maria Vittoria Struglia, Alcide Giorgio Di Sarra, Carlo Gomez, and Giuliana Mattiazzo. "Satellite Multi/Hyper Spectral HR Sensors for Mapping the Posidonia oceanica in South Mediterranean Islands." Sustainability 13, no. 24 (December 12, 2021): 13715. http://dx.doi.org/10.3390/su132413715.

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The Mediterranean basin is a hot spot of climate change where the Posidonia oceanica (L.) Delile (PO) and other seagrasses are under stress due to its effect on marine coastal habitats and the rising influence of anthropogenic activities (i.e., tourism, fishery). The PO and seabed ecosystems, in the coastal environments of Pantelleria and Lampedusa, suffer additional growing impacts from tourism in synergy with specific stress factors due to increasing vessel traffic for supplying potable water and fossil fuels for electrical power generation. Earth Observation (EO) data, provided by high resolution (HR) multi/hyperspectral operative satellite sensors of the last generation (i.e., Sentinel 2 MSI and PRISMA) have been successfully tested, using innovative calibration and sea truth collecting methods, for monitoring and mapping of PO meadows under stress, in the coastal waters of these islands, located in the Sicily Channel, to better support the sustainable management of these vulnerable ecosystems. The area of interest in Pantelleria was where the first prototype of the Italian Inertial Sea Wave Energy Converter (ISWEC) for renewable energy production was installed in 2015, and sea truth campaigns on the PO meadows were conducted. The PO of Lampedusa coastal areas, impacted by ship traffic linked to the previous factors and tropicalization effects of Italy’s southernmost climate change transitional zone, was mapped through a multi/hyper spectral EO-based approach, using training/testing data provided by side scan sonar data, previously acquired. Some advanced machine learning algorithms (MLA) were successfully evaluated with different supervised regression/classification models to map seabed and PO meadow classes and related Leaf Area Index (LAI) distributions in the areas of interest, using multi/hyperspectral data atmospherically corrected via different advanced approaches.
25

Sarkar, Soham, Swagatam Das, and Sheli Sinha Chaudhuri. "Hyper-spectral image segmentation using Rényi entropy based multi-level thresholding aided with differential evolution." Expert Systems with Applications 50 (May 2016): 120–29. http://dx.doi.org/10.1016/j.eswa.2015.11.016.

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26

Zhou, Jing, Biwen Wang, Jiahao Fan, Yuchi Ma, Yi Wang, and Zhou Zhang. "A Systematic Study of Estimating Potato N Concentrations Using UAV-Based Hyper- and Multi-Spectral Imagery." Agronomy 12, no. 10 (October 17, 2022): 2533. http://dx.doi.org/10.3390/agronomy12102533.

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Potato growth depends largely on nitrogen (N) availability in the soil. However, the shallow-root crop coupled with its common cultivation in coarse-textured soils leads to its poor N use efficiency. Fast and accurate estimations of potato tissue N concentrations are urgently needed to assist the decision making in precision fertilization management. Remote sensing has been utilized to evaluate the potato N status by correlating spectral information with lab tests on leaf N concentrations. In this study, a systematic comparison was conducted to quantitatively evaluate the performance of hyperspectral and multispectral images in estimating the potato N status, providing a reference for the trade-off between sensor costs and performance. In the experiment, two potato varieties were planted under four fertilization rates with replicates. UAV images were acquired multiple times during the season with a narrow-band hyperspectral imager. Multispectral reflectance was simulated by merging the relevant narrow bands into broad bands to mimic commonly used multispectral cameras. The whole leaf total N concentration and petiole nitrate-N concentration were obtained from 160 potato leaf samples. A partial least square regression model was developed to estimate the two N status indicators using different groups of image features. The best estimation accuracies were given by reflectance of the full spectra with 2.2 nm narrow, with the coefficient of determination (R2) being 0.78 and root mean square error (RMSE) being 0.41 for the whole leaf total N concentration; while, for the petiole nitrate-N concentration, the 10 nm bands had the best performance (R2 = 0.87 and RMSE = 0.13). Generally, the model performance decreased with an increase of the spectral bandwidth. The hyperspectral full spectra largely outperformed all three multispectral cameras, but there was no significant difference among the three brands of multispectral cameras. The results also showed that spectral bands in the visible regions (400–700 nm) were the most highly correlated with potato N concentrations.
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Sun, Y., Y. Lin, X. Hu, S. Zhao, S. Liu, Q. Tong, D. Helder, and L. Yan. "THE STUDY OF SPECTRUM RECONSTRUCTION BASED ON FUZZY SET FULL CONSTRAINT AND MULTIENDMEMBER DECOMPOSITION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 551–55. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-551-2017.

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Hyperspectral imaging system can obtain spectral and spatial information simultaneously with bandwidth to the level of 10 nm or even less. Therefore, hyperspectral remote sensing has the ability to detect some kinds of objects which can not be detected in wide-band remote sensing, making it becoming one of the hottest spots in remote sensing. In this study, under conditions with a fuzzy set of full constraints, Normalized Multi-Endmember Decomposition Method (NMEDM) for vegetation, water, and soil was proposed to reconstruct hyperspectral data using a large number of high-quality multispectral data and auxiliary spectral library data. This study considered spatial and temporal variation and decreased the calculation time required to reconstruct the hyper-spectral data. The results of spectral reconstruction based on NMEDM showed that the reconstructed data has good qualities and certain applications, which makes it possible to carry out spectral features identification. This method also extends the application of depth and breadth of remote sensing data, helping to explore the law between multispectral and hyperspectral data.
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Yang, Geng, Qin Li, Yu Yun, Yu Lei, and Jane You. "Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering." Electronics 12, no. 19 (September 29, 2023): 4083. http://dx.doi.org/10.3390/electronics12194083.

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Graph-based semi-supervised multi-view clustering has demonstrated promising performance and gained significant attention due to its capability to handle sample spaces with arbitrary shapes. Nevertheless, the ordinary graph employed by most existing semi-supervised multi-view clustering methods only captures the pairwise relationships between samples, and cannot fully explore the higher-order information and complex structure among multiple sample points. Additionally, most existing methods do not make full use of the complementary information and spatial structure contained in multi-view data, which is crucial to clustering results. We propose a novel hypergraph learning-based semi-supervised multi-view spectral clustering approach to overcome these limitations. Specifically, the proposed method fully considers the relationship between multiple sample points and utilizes hypergraph-induced hyper-Laplacian matrices to preserve the high-order geometrical structure in data. Based on the principle of complementarity and consistency between views, this method simultaneously learns indicator matrices of all views and harnesses the tensor Schatten p-norm to extract both complementary information and low-rank spatial structure within these views. Furthermore, we introduce an auto-weighted strategy to address the discrepancy between singular values, enhancing the robustness and stability of the algorithm. Detailed experimental results on various datasets demonstrate that our approach surpasses existing state-of-the-art semi-supervised multi-view clustering methods.
29

Wei, Xing, Jinnuo Zhang, Anna O. Conrad, Charles E. Flower, Cornelia C. Pinchot, Nancy Hayes-Plazolles, Ziling Chen, Zhihang Song, Songlin Fei, and Jian Jin. "Machine learning-based spectral and spatial analysis of hyper- and multi-spectral leaf images for Dutch elm disease detection and resistance screening." Artificial Intelligence in Agriculture 10 (December 2023): 26–34. http://dx.doi.org/10.1016/j.aiia.2023.09.003.

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Steddom, K., G. Heidel, D. Jones, and C. M. Rush. "Remote Detection of Rhizomania in Sugar Beets." Phytopathology® 93, no. 6 (June 2003): 720–26. http://dx.doi.org/10.1094/phyto.2003.93.6.720.

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As a prelude to remote sensing of rhizomania, hyper-spectral leaf reflectance and multi-spectral canopy reflectance were used to study the physiological differences between healthy sugar beets and beets infested with Beet necrotic yellow vein virus. This study was conducted over time in the presence of declining nitrogen levels. Total leaf nitrogen was significantly lower in symptomatic beets than in healthy beets. Chlorophyll and carotenoid levels were reduced in symptomatic beets. Vegetative indices calculated from leaf spectra showed reductions in chlorophyll and carotenoids in symptomatic beets. Betacyanin levels estimated from leaf spectra were decreased at the end of the 2000 season and not in 2001. The ratio of betacyanins to chlorophyll, estimated from canopy spectra, was increased in symptomatic beets at four of seven sampling dates. Differences in betacyanin and carotenoid levels appeared to be related to disease and not nitrogen content. Vegetative indices calculated from multi-spectral canopy spectra supported results from leaf spectra. Logistic regression models that incorporate vegetative indices and reflectance correctly predicted 88.8% of the observations from leaf spectra and 87.9% of the observations for canopy reflectance into healthy or symptomatic classes. Classification was best in August with a gradual decrease in accuracy until harvest. These results indicate that remote sensing technologies can facilitate detection of rhizomania.
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Zhang, L., Y. Zhong, B. Huang, and P. Li. "A resource limited artificial immune system algorithm for supervised classification of multi/hyper‐spectral remote sensing imagery." International Journal of Remote Sensing 28, no. 7 (April 2007): 1665–86. http://dx.doi.org/10.1080/01431160600675903.

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Kalidindi, Kishore Raju, Pardha Saradhi Varma Gottumukkala, and Rajyalakshmi Davuluri. "Derivative-based band clustering and multi-agent PSO optimization for optimal band selection of hyper-spectral images." Journal of Supercomputing 76, no. 8 (November 12, 2019): 5873–98. http://dx.doi.org/10.1007/s11227-019-03058-3.

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Xie, Busheng, Lixin Wu, Wenfei Mao, Shengyu Zhou, and Shanjun Liu. "An Open Integrated Rock Spectral Library (RockSL) for a Global Sharing and Matching Service." Minerals 12, no. 2 (January 20, 2022): 118. http://dx.doi.org/10.3390/min12020118.

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Minerals and rocks are important natural resources that are formed over a long period of geological history. Spectroscopy is the basis of the identification and characterisation of rocks and minerals via proximal sensing in the field or remote sensing systems with multi- and hyper-spectral capabilities. However, spectral data is scattered around different institutions worldwide and stored in various formats, resulting in poor data usability and an unnecessary waste of time and information. To improve the usability and performance of mineral spectral data, we developed an integrated open mineral spectral library (Rock Spectral Library, RockSL). Shared spectral data and related information were collected worldwide, and data cleaning measures were performed to retain the qualified spectra and merge all qualified data (raster, vector, and text formats) in a common framework to establish a reliable and comprehensive digital data set for an easy sharing and matching service. A software system was developed for the RockSL to manage, analyse, and apply the spectral data of minerals and rocks. We demonstrate how the information encoded in RockSL can determine the species of unknown rocks and describe specific mineral compositions. We also provide a reference scheme of the work chain and present key technologies for building different spectral libraries in diverse fields using RockSL. New contributions to RockSL are encouraged for this work to be improved to provide a better service and extend the applications of geo-sciences. This article introduces the characteristics of RockSL and demonstrates an experimental application.
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Leroux, Denis, Florian Alonso, Tanguy Coent, Maeva Garros, Régis Montvernay, Yann Le Bihan, and Corine Fulchiron. "Multispectral imaging for pathogen identification using a filter wheel and smartphone: A frugal innovation approach." EPJ Web of Conferences 287 (2023): 03017. http://dx.doi.org/10.1051/epjconf/202328703017.

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To create an affordable clinical test for use in more decentralized settings, we are developing a multispectral imaging system based on a filter wheel and a smartphone. Our application is the label-free identification of uropathogens from images of bacterial colonies directly on a non-chromogenic culture medium. Using feature selection techniques, we have previously shown through calculations, the possibility to move from hyper- to multi- spectral imaging by exploiting less than 8 spectral bands. Here, we confirm our findings by testing a database of true multispectral images acquired using a filter wheel holding up to 22 dichroic bandpass filters with a 10 nm bandwidth. Performance is reported for 5 species using only 6 filters. Probabilistic SVM algorithms were implemented to allow to reject species other than the targeted most prevalent uropathogens as it is crucial to keep the false-positive rate low. Evolution of specificity and sensitivity with probability threshold are discussed in the light of probability frequency distributions.
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Palleschi, Vincenzo, Luciano Marras, and Maria Angela Turchetti. "Interesting Features Finder: A New Approach to Multispectral Image Analysis." Heritage 5, no. 4 (December 11, 2022): 4089–99. http://dx.doi.org/10.3390/heritage5040211.

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In this paper, we discuss a new approach to the analysis of multi/hyper-spectral data sets, based on the Interesting Features Finder (IFF) method. The IFF is a simple algorithm recently proposed in the framework of Laser-Induced Breakdown Spectroscopy (LIBS) spectral analysis for detecting ‘interesting’ spectral features independently of the variance they represent in a set of spectra. To test the usefulness of this method to multispectral analysis, we show in this paper the results of its application on the recovery of a ‘lost’ painting from the Etruscan hypogeal tomb of the Volumni (3rd century BCE—1st century CE) in Perugia, Italy. The results obtained applying the IFF algorithm are compared with the results obtained by applying Blind Source Separation (BSS) techniques and Self-Organized Maps (SOM) to a multispectral set of 17 fluorescence and reflection images. From this comparison emerges the possibility of using the IFF algorithm to obtain rapidly and simultaneously, by varying a single parameter in a range from 0 to 1, several sets of elaborated images all containing the ‘interesting’ features and carrying information comparable to what could have been obtained by BSS and SOM, respectively.
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Phinn, Stuart, Chris Roelfsema, Arnold Dekker, Vittoro Brando, and Janet Anstee. "Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia)." Remote Sensing of Environment 112, no. 8 (August 2008): 3413–25. http://dx.doi.org/10.1016/j.rse.2007.09.017.

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Mirzaie, M., R. Darvishzadeh, A. Shakiba, A. A. Matkan, C. Atzberger, and A. Skidmore. "Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements." International Journal of Applied Earth Observation and Geoinformation 26 (February 2014): 1–11. http://dx.doi.org/10.1016/j.jag.2013.04.004.

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Adamopoulos, Efstathios, and Fulvio Rinaudo. "UAS-Based Archaeological Remote Sensing: Review, Meta-Analysis and State-of-the-Art." Drones 4, no. 3 (August 19, 2020): 46. http://dx.doi.org/10.3390/drones4030046.

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Over the last decade, we have witnessed momentous technological developments in unmanned aircraft systems (UAS) and in lightweight sensors operating at various wavelengths, at and beyond the visible spectrum, which can be integrated with unmanned aerial platforms. These innovations have made feasible close-range and high-resolution remote sensing for numerous archaeological applications, including documentation, prospection, and monitoring bridging the gap between satellite, high-altitude airborne, and terrestrial sensing of historical sites and landscapes. In this article, we track the progress made so far, by systematically reviewing the literature relevant to the combined use of UAS platforms with visible, infrared, multi-spectral, hyper-spectral, laser, and radar sensors to reveal archaeological features otherwise invisible to archaeologists with applied non-destructive techniques. We review, specific applications and their global distribution, as well as commonly used platforms, sensors, and data-processing workflows. Furthermore, we identify the contemporary state-of-the-art and discuss the challenges that have already been overcome, and those that have not, to propose suggestions for future research.
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Malinee, Rachane, Dimitris Stratoulias, and Narissara Nuthammachot. "Detection of Oil Palm Disease in Plantations in Krabi Province, Thailand with High Spatial Resolution Satellite Imagery." Agriculture 11, no. 3 (March 16, 2021): 251. http://dx.doi.org/10.3390/agriculture11030251.

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Oil palm (Elaeis guineensis) trees are an important contributor of recent economic development in Southeast Asia. The high product yield, and the consequent high profitability, has led to a widespread expansion of plantations in the greater region. However, oil palms are susceptible to diseases that can have a detrimental effect. In this study we use hyper- and multi-spectral remote sensing to detect diseased oil palm trees in Krabi province, Thailand. Proximate spectroscopic measurements were used to identify and discern differences in leaf spectral radiance; the results indicate a relatively higher radiance in visible and near-infrared for the healthy leaves in comparison to the diseased. From a total of 113 samples for which the geolocation and the hyperspectral radiance were recorded, 59 and 54 samples were healthy and diseased oil palm trees, respectively. Moreover, a WorldView-2 satellite image was used to investigate the usability of traditional vegetation indices and subsequently detecting diseased oil palm trees. The results show that the overall maximum likelihood classification accuracy is 85.98%, the Kappa coefficient 0.71 and the producer’s accuracy for healthy and diseased oil palm trees 83.33 and 78.95, respectively. We conclude that high spatial and spectral resolutions can play a vital role in monitoring diseases in oil palm plantations.
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Tan, Fei, Xiaoming Mo, Shiwei Ruan, Tianying Yan, Peng Xing, Pan Gao, Wei Xu, et al. "Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes." Foods 12, no. 19 (September 28, 2023): 3621. http://dx.doi.org/10.3390/foods12193621.

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Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R2 of the multi-quality prediction model established by Vis-NIR spectra is higher than that of NIR spectra. The R2 of the best model obtained by SSC and TA fusion band is greater than 0.9, and that of the best model obtained by the firmness fusion band is greater than 0.85. It is better to use the spectral bands after information fusion for nondestructive quality detection of cherry tomatoes. This study shows that hyperspectral imaging technology can be used for the nondestructive detection of multiple qualities of cherry tomatoes, and the method based on the fusion of two spectra has a better prediction effect for the rapid detection of multiple qualities of cherry tomatoes compared with a single spectrum. This study can provide certain technical support for the rapid nondestructive detection of multiple qualities in other melons and fruits.
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Laureti, Stefano, Hamed Malekmohammadi, Muhammad Khalid Rizwan, Pietro Burrascano, Stefano Sfarra, Miranda Mostacci, and Marco Ricci. "Looking Through Paintings by Combining Hyper-Spectral Imaging and Pulse-Compression Thermography." Sensors 19, no. 19 (October 8, 2019): 4335. http://dx.doi.org/10.3390/s19194335.

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The use of different spectral bands in the inspection of artworks is highly recommended to identify the maximum number of defects/anomalies (i.e., the targets), whose presence ought to be known before any possible restoration action. Although an artwork cannot be considered as a composite material in which the zero-defect theory is usually followed by scientists, it is possible to state that the preservation of a multi-layered structure fabricated by the artist’s hands is based on a methodological analysis, where the use of non-destructive testing methods is highly desirable. In this paper, the infrared thermography and hyperspectral imaging methods were applied to identify both fabricated and non-fabricated targets in a canvas painting mocking up the famous character “Venus” by Botticelli. The pulse-compression thermography technique was used to retrieve info about the inner structure of the sample and low power light-emitting diode (LED) chips, whose emission was modulated via a pseudo-noise sequence, were exploited as the heat source for minimizing the heat radiated on the sample surface. Hyper-spectral imaging was employed to detect surface and subsurface features such as pentimenti and facial contours. The results demonstrate how the application of statistical algorithms (i.e., principal component and independent component analyses) maximized the number of targets retrieved during the post-acquisition steps for both the employed techniques. Finally, the best results obtained by both techniques and post-processing methods were fused together, resulting in a clear targets map, in which both the surface, subsurface and deeper information are all shown at a glance.
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John, C. M., and N. Kavya. "Integration of multispectral satellite and hyperspectral field data for aquatic macrophyte studies." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 581–88. http://dx.doi.org/10.5194/isprsarchives-xl-8-581-2014.

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Aquatic macrophytes (AM) can serve as useful indicators of water pollution along the littoral zones. The spectral signatures of various AM were investigated to determine whether species could be discriminated by remote sensing. In this study the spectral readings of different AM communities identified were done using the ASD Fieldspec® Hand Held spectro-radiometer in the wavelength range of 325–1075 nm. The collected specific reflectance spectra were applied to space borne multi-spectral remote sensing data from Worldview-2, acquired on 26th March 2011. The dimensionality reduction of the spectro-radiometric data was done using the technique principal components analysis (PCA). Out of the different PCA axes generated, 93.472 % variance of the spectra was explained by the first axis. The spectral derivative analysis was done to identify the wavelength where the greatest difference in reflectance is shown. The identified wavelengths are 510, 690, 720, 756, 806, 885, 907 and 923 nm. The output of PCA and derivative analysis were applied to Worldview-2 satellite data for spectral subsetting. The unsupervised classification was used to effectively classify the AM species using the different spectral subsets. The accuracy assessment of the results of the unsupervised classification and their comparison were done. The overall accuracy of the result of unsupervised classification using the band combinations Red-Edge, Green, Coastal blue & Red-edge, Yellow, Blue is 100%. The band combinations NIR-1, Green, Coastal blue & NIR-1, Yellow, Blue yielded an accuracy of 82.35 %. The existing vegetation indices and new hyper-spectral indices for the different type of AM communities were computed. Overall, results of this study suggest that high spectral and spatial resolution images provide useful information for natural resource managers especially with regard to the location identification and distribution mapping of macrophyte species and their communities.
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Gao, Meng, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Brian Cairns, Xiaoguang Xu, and J. Vanderlei Martins. "The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color." Atmospheric Measurement Techniques 16, no. 8 (April 19, 2023): 2067–87. http://dx.doi.org/10.5194/amt-16-2067-2023.

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Abstract. Multi-angle polarimetric (MAP) measurements contain rich information for characterization of aerosol microphysical and optical properties that can be used to improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system, although uncertainty correlation among measurements is generally ignored due to lack of knowledge on its strength and characterization. In this work, we provide a practical framework to evaluate the impact of the angular uncertainty correlation from retrieval results and a method to estimate correlation strength from retrieval fitting residuals. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on neural-network forward models, is used to conduct the retrievals and uncertainty quantification. In addition, we also discuss a flexible approach to include a correlated uncertainty model in the retrieval algorithm. The impact of angular correlation on retrieval uncertainties is discussed based on synthetic Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) and Hyper-Angular Rainbow Polarimeter 2 (HARP2) measurements using a Monte Carlo uncertainty estimation method. Correlation properties are estimated using autocorrelation functions based on the fitting residuals from both synthetic AirHARP and HARP2 data and real AirHARP measurement, with the resulting angular correlation parameters found to be larger than 0.9 and 0.8 for reflectance and degree of linear polarization (DoLP), respectively, which correspond to correlation angles of 10 and 5∘. Although this study focuses on angular correlation from HARP instruments, the methodology to study and quantify uncertainty correlation is also applicable to other instruments with angular, spectral, or spatial correlations and can help inform laboratory calibration and characterization of the instrument uncertainty structure.
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Bernhardt, V., A. Grumpe, and C. Wöhler. "SPECTRAL UNMIXING BASED CONSTRUCTION OF LUNAR MINERAL ABUNDANCE MAPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W1 (July 25, 2017): 7–14. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w1-7-2017.

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In this study we apply a nonlinear spectral unmixing algorithm to a nearly global lunar spectral reflectance mosaic derived from hyper-spectral image data acquired by the Moon Mineralogy Mapper (M<sup>3</sup>) instrument. Corrections for topographic effects and for thermal emission were performed. A set of 19 laboratory-based reflectance spectra of lunar samples published by the Lunar Soil Characterization Consortium (LSCC) were used as a catalog of potential endmember spectra. For a given spectrum, the multi-population population-based incremental learning (MPBIL) algorithm was used to determine the subset of endmembers actually contained in it. However, as the MPBIL algorithm is computationally expensive, it cannot be applied to all pixels of the reflectance mosaic. Hence, the reflectance mosaic was clustered into a set of 64 prototype spectra, and the MPBIL algorithm was applied to each prototype spectrum. Each pixel of the mosaic was assigned to the most similar prototype, and the set of endmembers previously determined for that prototype was used for pixel-wise nonlinear spectral unmixing using the Hapke model, implemented as linear unmixing of the single-scattering albedo spectrum. This procedure yields maps of the fractional abundances of the 19 endmembers. Based on the known modal abundances of a variety of mineral species in the LSCC samples, a conversion from endmember abundances to mineral abundances was performed. We present maps of the fractional abundances of plagioclase, pyroxene and olivine and compare our results with previously published lunar mineral abundance maps.
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Sun, Changli, and Jiangang Lu. "Effect of Sectional Polymerization Process on Tunable Twist Structure Liquid Crystal Filters." Crystals 9, no. 5 (May 23, 2019): 268. http://dx.doi.org/10.3390/cryst9050268.

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The effect of sectional polymerization process on tunable filters with cholesteric liquid crystal (CLC) and blue phase liquid crystal (BPLC) is demonstrated. The bandwidths of the polymer-stabilized cholesteric liquid crystal (PSCLC) and polymer-stabilized blue phase liquid crystal (PSBPLC) filters can be broadened by the holding treatment without distortion. The reflection bandwidth of the CLC filter can be broadened from 120 nm to 220 nm, and that of the BPLC filter can be broadened from 45 nm to 140 nm. Meanwhile, the intensity of reflection can be retained very well. The central wavelength of polymer-stabilized CLC filter can be thermally tuned from 1614 nm to 1460 nm with a stable wide bandwidth. The tunable C-band CLC filter and BPLC filter show great potential application in multi- and hyper-spectral systems and wide-band color filters.
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Lane, David W., Antony Nyombi, and James Shackel. "Energy-dispersive X-ray diffraction mapping on a benchtop X-ray fluorescence system." Journal of Applied Crystallography 47, no. 2 (February 22, 2014): 488–94. http://dx.doi.org/10.1107/s1600576714000314.

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A method for energy-dispersive X-ray diffraction mapping is presented, using a conventional low-power benchtop X-ray fluorescence spectrometer, the Seiko Instruments SEA6000VX. Hyper spectral X-ray maps with a 10 µm step size were collected from polished metal surfaces, sectioned Bi, Pb and steel shot gun pellets. Candidate diffraction lines were identified by eliminating those that matched a characteristic line for an element and those predicted for escape peaks, sum peaks, and Rayleigh and Compton scattered primary X-rays. The maps showed that the crystallites in the Bi pellet were larger than those observed in the Pb and steel pellets. The application of benchtop spectrometers to energy-dispersive X-ray diffraction mapping is discussed, and the capability for lower atomic number and lower-symmetry materials is briefly explored using multi-crystalline Si and polycrystalline sucrose.
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Han, Xian-Hua, Yongqing Sun, Jian Wang, Boxin Shi, Yinqiang Zheng, and Yen-Wei Chen. "Spectral Representation via Data-Guided Sparsity for Hyperspectral Image Super-Resolution." Sensors 19, no. 24 (December 7, 2019): 5401. http://dx.doi.org/10.3390/s19245401.

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Hyperspectral imaging is capable of acquiring the rich spectral information of scenes and has great potential for understanding the characteristics of different materials in many applications ranging from remote sensing to medical imaging. However, due to hardware limitations, the existed hyper-/multi-spectral imaging devices usually cannot obtain high spatial resolution. This study aims to generate a high resolution hyperspectral image according to the available low resolution hyperspectral and high resolution RGB images. We propose a novel hyperspectral image superresolution method via non-negative sparse representation of reflectance spectra with a data guided sparsity constraint. The proposed method firstly learns the hyperspectral dictionary from the low resolution hyperspectral image and then transforms it into the RGB one with the camera response function, which is decided by the physical property of the RGB imaging camera. Given the RGB vector and the RGB dictionary, the sparse representation of each pixel in the high resolution image is calculated with the guidance of a sparsity map, which measures pixel material purity. The sparsity map is generated by analyzing the local content similarity of a focused pixel in the available high resolution RGB image and quantifying the spectral mixing degree motivated by the fact that the pixel spectrum of a pure material should have sparse representation of the spectral dictionary. Since the proposed method adaptively adjusts the sparsity in the spectral representation based on the local content of the available high resolution RGB image, it can produce more robust spectral representation for recovering the target high resolution hyperspectral image. Comprehensive experiments on two public hyperspectral datasets and three real remote sensing images validate that the proposed method achieves promising performances compared to the existing state-of-the-art methods.
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Olivetti, Diogo, Rejane Cicerelli, Jean-Michel Martinez, Tati Almeida, Raphael Casari, Henrique Borges, and Henrique Roig. "Comparing Unmanned Aerial Multispectral and Hyperspectral Imagery for Harmful Algal Bloom Monitoring in Artificial Ponds Used for Fish Farming." Drones 7, no. 7 (June 21, 2023): 410. http://dx.doi.org/10.3390/drones7070410.

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This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.
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Kim, Jonghyun, Kyeonghoon Jeong, and Moon Gi Kang. "Crosstalk Correction for Color Filter Array Image Sensors Based on Lp-Regularized Multi-Channel Deconvolution." Sensors 22, no. 11 (June 4, 2022): 4285. http://dx.doi.org/10.3390/s22114285.

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In this paper, we propose a crosstalk correction method for color filter array (CFA) image sensors based on Lp-regularized multi-channel deconvolution. Most imaging systems with CFA exhibit a crosstalk phenomenon caused by the physical limitations of the image sensor. In general, this phenomenon produces both color degradation and spatial degradation, which are respectively called desaturation and blurring. To improve the color fidelity and the spatial resolution in crosstalk correction, the feasible solution of the ill-posed problem is regularized by image priors. First, the crosstalk problem with complex spatial and spectral degradation is formulated as a multi-channel degradation model. An objective function with a hyper-Laplacian prior is then designed for crosstalk correction. This approach enables the simultaneous improvement of the color fidelity and the sharpness restoration of the details without noise amplification. Furthermore, an efficient solver minimizes the objective function for crosstalk correction consisting of Lp regularization terms. The proposed method was verified on synthetic datasets according to various crosstalk and noise levels. Experimental results demonstrated that the proposed method outperforms the conventional methods in terms of the color peak signal-to-noise ratio and structural similarity index measure.
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Zhang, Zhengxin, and Lixue Zhu. "A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications." Drones 7, no. 6 (June 15, 2023): 398. http://dx.doi.org/10.3390/drones7060398.

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In recent years, UAV remote sensing has gradually attracted the attention of scientific researchers and industry, due to its broad application prospects. It has been widely used in agriculture, forestry, mining, and other industries. UAVs can be flexibly equipped with various sensors, such as optical, infrared, and LIDAR, and become an essential remote sensing observation platform. Based on UAV remote sensing, researchers can obtain many high-resolution images, with each pixel being a centimeter or millimeter. The purpose of this paper is to investigate the current applications of UAV remote sensing, as well as the aircraft platforms, data types, and elements used in each application category; the data processing methods, etc.; and to study the advantages of the current application of UAV remote sensing technology, the limitations, and promising directions that still lack applications. By reviewing the papers published in this field in recent years, we found that the current application research of UAV remote sensing research can be classified into four categories according to the application field: (1) Precision agriculture, including crop disease observation, crop yield estimation, and crop environmental observation; (2) Forestry remote sensing, including forest disease identification, forest disaster observation, etc.; (3) Remote sensing of power systems; (4) Artificial facilities and the natural environment. We found that in the papers published in recent years, image data (RGB, multi-spectral, hyper-spectral) processing mainly used neural network methods; in crop disease monitoring, multi-spectral data are the most studied type of data; for LIDAR data, current applications still lack an end-to-end neural network processing method; this review examines UAV platforms, sensors, and data processing methods, and according to the development process of certain application fields and current implementation limitations, some predictions are made about possible future development directions.

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