Search

Your search keyword '"Matsushita, Bunkei"' showing total 243 results
Start Over Author "Matsushita, Bunkei"
243 results on '"Matsushita, Bunkei"'

1. Author Correction: GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

Author

Lehmann, Moritz K, Gurlin, Daniela, Pahlevan, Nima, Alikas, Krista, Conroy, Ted, Anstee, Janet, Balasubramanian, Sundarabalan V, Barbosa, Cláudio CF, Binding, Caren, Bracher, Astrid, Bresciani, Mariano, Burtner, Ashley, Cao, Zhigang, Dekker, Arnold G, Di Vittorio, Courtney, Drayson, Nathan, Errera, Reagan M, Fernandez, Virginia, Ficek, Dariusz, Fichot, Cédric G, Gege, Peter, Giardino, Claudia, Gitelson, Anatoly A, Greb, Steven R, Henderson, Hayden, Higa, Hiroto, Rahaghi, Abolfazl Irani, Jamet, Cédric, Jiang, Dalin, Jordan, Thomas, Kangro, Kersti, Kravitz, Jeremy A, Kristoffersen, Arne S, Kudela, Raphael, Li, Lin, Ligi, Martin, Loisel, Hubert, Lohrenz, Steven, Ma, Ronghua, Maciel, Daniel A, Malthus, Tim J, Matsushita, Bunkei, Matthews, Mark, Minaudo, Camille, Mishra, Deepak R, Mishra, Sachidananda, Moore, Tim, Moses, Wesley J, Nguyễn, Hà, Novo, Evlyn MLM, Novoa, Stéfani, Odermatt, Daniel, O’Donnell, David M, Olmanson, Leif G, Ondrusek, Michael, Oppelt, Natascha, Ouillon, Sylvain, Pereira Filho, Waterloo, Plattner, Stefan, Verdú, Antonio Ruiz, Salem, Salem I, Schalles, John F, Simis, Stefan GH, Siswanto, Eko, Smith, Brandon, Somlai-Schweiger, Ian, Soppa, Mariana A, Spyrakos, Evangelos, Tessin, Elinor, van der Woerd, Hendrik J, Vander Woude, Andrea, Vandermeulen, Ryan A, Vantrepotte, Vincent, Wernand, Marcel R, Werther, Mortimer, Young, Kyana, and Yue, Linwei

Subjects
Data Management and Data Science, Information and Computing Sciences
Published
2023

2. GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

Author

Lehmann, Moritz K, Gurlin, Daniela, Pahlevan, Nima, Alikas, Krista, Conroy, Ted, Anstee, Janet, Balasubramanian, Sundarabalan V, Barbosa, Cláudio CF, Binding, Caren, Bracher, Astrid, Bresciani, Mariano, Burtner, Ashley, Cao, Zhigang, Dekker, Arnold G, Di Vittorio, Courtney, Drayson, Nathan, Errera, Reagan M, Fernandez, Virginia, Ficek, Dariusz, Fichot, Cédric G, Gege, Peter, Giardino, Claudia, Gitelson, Anatoly A, Greb, Steven R, Henderson, Hayden, Higa, Hiroto, Rahaghi, Abolfazl Irani, Jamet, Cédric, Jiang, Dalin, Jordan, Thomas, Kangro, Kersti, Kravitz, Jeremy A, Kristoffersen, Arne S, Kudela, Raphael, Li, Lin, Ligi, Martin, Loisel, Hubert, Lohrenz, Steven, Ma, Ronghua, Maciel, Daniel A, Malthus, Tim J, Matsushita, Bunkei, Matthews, Mark, Minaudo, Camille, Mishra, Deepak R, Mishra, Sachidananda, Moore, Tim, Moses, Wesley J, Nguyễn, Hà, Novo, Evlyn MLM, Novoa, Stéfani, Odermatt, Daniel, O’Donnell, David M, Olmanson, Leif G, Ondrusek, Michael, Oppelt, Natascha, Ouillon, Sylvain, Pereira Filho, Waterloo, Plattner, Stefan, Verdú, Antonio Ruiz, Salem, Salem I, Schalles, John F, Simis, Stefan GH, Siswanto, Eko, Smith, Brandon, Somlai-Schweiger, Ian, Soppa, Mariana A, Spyrakos, Evangelos, Tessin, Elinor, van der Woerd, Hendrik J, Vander Woude, Andrea, Vandermeulen, Ryan A, Vantrepotte, Vincent, Wernand, Marcel R, Werther, Mortimer, Young, Kyana, and Yue, Linwei

Subjects
Earth Sciences, Geomatic Engineering, Engineering
Abstract

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Published
2023

7. Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3

Author

Pahlevan, Nima, Smith, Brandon, Alikas, Krista, Anstee, Janet, Barbosa, Claudio, Binding, Caren, Bresciani, Mariano, Cremella, Bruno, Giardino, Claudia, Gurlin, Daniela, Fernandez, Virginia, Jamet, Cédric, Kangro, Kersti, Lehmann, Moritz K., Loisel, Hubert, Matsushita, Bunkei, Hà, Nguyên, Olmanson, Leif, Potvin, Geneviève, Simis, Stefan G.H., VanderWoude, Andrea, Vantrepotte, Vincent, and Ruiz-Verdù, Antonio

Published
2022
Full Text
View/download PDF

16. Optical types of inland and coastal waters

Author

Spyrakos, Evangelos, O’Donnell, Ruth, Hunter, Peter D., Miller, Claire, Scott, Marian, Simis, Stefan G. H., Neil, Claire, Barbosa, Claudio C. F., Binding, Caren E., Bradt, Shane, Bresciani, Mariano, Dall’Olmo, Giorgio, Giardino, Claudia, Gitelson, Anatoly A., Kutser, Tiit, Li, Lin, Matsushita, Bunkei, Martinez-Vicente, Victor, Matthews, Mark W., Ogashawara, Igor, Ruiz-Verdú, Antonio, Schalles, John F., Tebbs, Emma, Zhang, Yunlin, and Tyler, Andrew N.

Published
2018

23. Robust Algorithm for Estimating Total Suspended Solids (TSS) in Inland and Nearshore Coastal Waters

Author

Sundarabalan V Balasubramanian, Nima Pahlevan, Brandon Smith, Caren Binding, John Schalles, Hubert Loisel, Daniela Gurlin, Steven Greb, Krista Alikas, Mirjam Randla, Matsushita Bunkei, Wesley Moses, Ha Nguyen, Moritz K Lehmann, David O'Donnell, Michael Ondrusek, Tai-Hyun Han, Cedric G Fichot, Tim Moore, and Emmanuel Boss

Subjects
Earth Resources And Remote Sensing
Abstract

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R(sub rs)), (b) retrieval of particulate backscattering (b(sub bp)) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b(sub bp) via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m (exp 3)], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e., from 10 to > 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads.

Published
2020
Full Text
View/download PDF

47. Generalization of subpixel analysis for hyperspectral data with flexibility in spectral similarity measures

Author

Chen, Jin, Jia, Xiuping, Yang, Wei, and Matsushita, Bunkei

Subjects
Algorithms -- Usage, Mathematical optimization -- Research, Image processing -- Methods, Algorithm, Business, Earth sciences, Electronics and electrical industries
Abstract

Several spectral unmixing techniques have been developed for subpixel mapping using hyperspectral data in the past two decades, among which the fully constrained least squares method based on the linear spectral mixture model (LSMM) has been widely accepted. However, the shortage of this method is that the Euclidean spectral distance measure is used, and therefore, it is sensitive to the magnitude of the spectra. While other spectral matching criteria are available, such as spectral angle mapping (SAM) and spectral information divergence (SID), the current unmixing algorithm is unable to be extended to these measures. In this paper, we propose a unified subpixel mapping framework that models the unmixing process as a best match of the unknown pixers spectrum to a weighted sum of the endmembers' spectra. We introduce sequential quadratic programming to solve the nonlinear optimization problem encountered in the implementation of this framework. The main feature of this proposed method is that it is not restricted to any particular similarity measures. Experiments were conducted with both simulated and Hyperion data. The tests demonstrated the proposed framework's advantage in accommodating various spectral similarity measures and provided performance comparisons of the Euclidean distance measure with other spectral matching criteria including SAM, spectral correlation measure, and SID. Index Terms--Constrained nonlinear optimization, sequential quadratic optimization, spectral mixture analysis, spectrum matching.

Published
2009

Catalog

Books, media, physical & digital resources
See catalog results