The Experts below are selected from a list of 190788 Experts worldwide ranked by ideXlab platform

Medhavy Thankappan - One of the best experts on this subject based on the ideXlab platform.

  • The SPECCHIO Spectral Information System
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
    Co-Authors: Andreas Hueni, Laurie A. Chisholm, Cindy Ong, Tim J. Malthus, Mathew Wyatt, Simon A. Trim, Michael E. Schaepman, Medhavy Thankappan
    Abstract:

    Spectral Information Systems provide a framework to assemble, curate, and serve Spectral data and their associated metadata. This article documents the evolution of the SPECCHIO system, devised to enable long-term usability and data-sharing of field spectroradiometer data. The new capabilities include a modern, web-based client-server architecture, a flexible metadata storage scheme for generic metadata handling, and a rich application programming interface, enabling scientists to directly access Spectral data and metadata from their programming environment of choice. The SPECCHIO system source code has been moved into the open source domain to stimulate contributions from the spectroscopy community while binary distributions, including the SPECCHIO virtual machine, simplify the installation and use of the system for the end-users.

Andreas Hueni - One of the best experts on this subject based on the ideXlab platform.

  • The SPECCHIO Spectral Information System
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
    Co-Authors: Andreas Hueni, Laurie A. Chisholm, Cindy Ong, Tim J. Malthus, Mathew Wyatt, Simon A. Trim, Michael E. Schaepman, Medhavy Thankappan
    Abstract:

    Spectral Information Systems provide a framework to assemble, curate, and serve Spectral data and their associated metadata. This article documents the evolution of the SPECCHIO system, devised to enable long-term usability and data-sharing of field spectroradiometer data. The new capabilities include a modern, web-based client-server architecture, a flexible metadata storage scheme for generic metadata handling, and a rich application programming interface, enabling scientists to directly access Spectral data and metadata from their programming environment of choice. The SPECCHIO system source code has been moved into the open source domain to stimulate contributions from the spectroscopy community while binary distributions, including the SPECCHIO virtual machine, simplify the installation and use of the system for the end-users.

  • Spectral Information system for Australian spectroscopy data
    2013
    Co-Authors: Laurie A. Chisholm, Andreas Hueni, Lola Suarez, Cindy Ong, Natalia Restrepo-coupe
    Abstract:

    Abstract presented at 2013 Fall Meeting, AGU, San Francisco, California, USA, 9-13 Dec. Disciplines Medicine and Health Sciences | Social and Behavioral Sciences Publication Details Chisholm, L. A., Ong, C., Hueni, A., Suarez, L. & Restrepo-Coupe, N. (2013). Spectral Information system for Australian spectroscopy data. Abstract of Atmospheric Sciences 2013 Fall Meeting United States: American Geophysical Union. This conference paper is available at Research Online: http://ro.uow.edu.au/smhpapers/1554

  • GSR - Spectral Information system development for Australia
    2012
    Co-Authors: Andreas Hueni, Laurie A. Chisholm, Lola Suarez, Cindy Ong, Matthew Wyatt
    Abstract:

    Scientific efforts to observe the state of natural systems over time, allowing the prediction of future states, have led to a burgeoning interest for organised storage of Spectral field data and associated metadata, seen as being key to the successful and efficient modeling of such systems. A centralised system for such data established for the Australian remote sensing community aims to standardise storage parameters and metadata thus fostering best practice protocols and collaborative research. Supported by the Australian National Data Service (ANDS), whose aim is to promote connections between data, projects, researchers and institutions, a Spectral Information system based on the already operational SPECCHIO Spectral database system is being augmented to specifically meet the needs of the Australian remote sensing community, and is aligned with the Terrestrial Ecosystem Research Network (TERN) Auscover facility. In this paper we outline the envisaged dataflow and usage of the system as a case study within the context of TERN Auscover. The development of a national Spectral Information system will not only ensure the long-term storage of data but support scientists in data analysis activities, essentially leading to improved repeatability of results, superior reprocessing capabilities, and promotion of best practice.

Michael E. Schaepman - One of the best experts on this subject based on the ideXlab platform.

  • The SPECCHIO Spectral Information System
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
    Co-Authors: Andreas Hueni, Laurie A. Chisholm, Cindy Ong, Tim J. Malthus, Mathew Wyatt, Simon A. Trim, Michael E. Schaepman, Medhavy Thankappan
    Abstract:

    Spectral Information Systems provide a framework to assemble, curate, and serve Spectral data and their associated metadata. This article documents the evolution of the SPECCHIO system, devised to enable long-term usability and data-sharing of field spectroradiometer data. The new capabilities include a modern, web-based client-server architecture, a flexible metadata storage scheme for generic metadata handling, and a rich application programming interface, enabling scientists to directly access Spectral data and metadata from their programming environment of choice. The SPECCHIO system source code has been moved into the open source domain to stimulate contributions from the spectroscopy community while binary distributions, including the SPECCHIO virtual machine, simplify the installation and use of the system for the end-users.

  • Using Spectral Information from the NIR water absorption features for the retrieval of canopy water content
    International Journal of Applied Earth Observation and Geoinformation, 2008
    Co-Authors: Jan G. P. W. Clevers, Lammert Kooistra, Michael E. Schaepman
    Abstract:

    Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral Information related to the water absorption features at 970 nm and 1200 nm offers possibilities for deriving Information on CWC. In this study, we compare the use of derivative spectra, Spectral indices and continuum removal techniques for these regions. HyperSpectral reflectance data representing a range of canopies were simulated using the combined PROSPECT + SAILH model. Best results in estimating CWC were obtained by using Spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features. Real data from two different test sites were analysed. Spectral Information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970 nm can best be used for estimating CWC.

Chi Farn Chen - One of the best experts on this subject based on the ideXlab platform.

  • Spectral Information Adaptation and Synthesis Scheme for Merging Cross-Mission Ocean Color Reflectance Observations from MODIS and VIIRS
    IEEE Transactions on Geoscience and Remote Sensing, 2016
    Co-Authors: Kaixu Bai, Ni-bin Chang, Chi Farn Chen
    Abstract:

    Obtaining a full clear view of coastal bays, estuaries, lakes, and inland waters is challenging with single satellite sensor observations due to cloud impacts. Cross-mission sensors provide the synergistic opportunity to improve spatial and temporal coverage by merging their observations; however, discrepancies originating from the instrumental, algorithmic, and temporal differences should be eliminated before merging. This paper presents the Spectral Information Adaptation and Synthesis Scheme (SIASS) for generating cross-mission consistent ocean color reflectance by merging 2012-2015 observations from Moderate Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite over Lake Nicaragua in Central America, where the cloud impact is salient. The SIASS is able to not only eliminate incompatibilities for matchup bands but also reconstruct Spectral Information for mismatched bands among sensors. Statistics indicate that the average monthly coverage of a merged ocean color reflectance product over Lake Nicaragua is nearly twice that of any single-sensor observation. Results show that SIASS significantly improves consistency among cross-mission sensors by mitigating prominent discrepancies. In addition, reconstructed Spectral Information for those mismatched bands help preserve more Spectral characteristics needed to better monitor and understand the dynamic aquatic environment. The final implementation of SIASS to map the chlorophyll-α concentration demonstrates the efficacy of SIASS in bias correction and consistency improvement. In general, SIASS can be applied to remove cross-mission discrepancies among sensors to improve the overall consistency.

  • Segmentation of high resolution satellite imagery using texture and Spectral Information
    Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium, 1
    Co-Authors: Chi Farn Chen
    Abstract:

    MultiSpectral data of Earth resource satellites has been widely used for automated land-cover-land-use classification since the launch of Landsat MSS. There are many classification algorithms, most of them are implemented by per-pixel classifier based on Spectral response. It is sufficient to use only the Spectral data to perform pixel-based classification for low resolution satellite imagery. However with the improvement of the spatial resolution of satellite image, e.g., 20 m and 10 m of SPOT, the detail of the image has become more complicated than that of low resolution image. It is apparent that the per-pixel approach with Spectral Information alone is inadequate for classifying high resolution data. This study describes a multiple level segmentation method which uses texture as well as Spectral Information for classification. The basic idea of this method is that at the first level of the segmentation moving window operation is performed for the whole image, then the Spectral statistics of the window is compared with a lookup table of a training set. The comparison will statistically determine the class of the window. If the class can be separated into more detail classes according to spatial Information, then segmentation continues to the second level. The spatial measurement is then used to perform comparison between moving window and spatial lookup table. Accordingly, the segmentation technique and additional spatial Information should be able to classify the image to more detailed level. The testing results indicate that the proposed multi-level segmentation approach and the use of spatial Information is feasible and useful in classifying high resolution satellite imagery. >

Laurie A. Chisholm - One of the best experts on this subject based on the ideXlab platform.

  • The SPECCHIO Spectral Information System
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
    Co-Authors: Andreas Hueni, Laurie A. Chisholm, Cindy Ong, Tim J. Malthus, Mathew Wyatt, Simon A. Trim, Michael E. Schaepman, Medhavy Thankappan
    Abstract:

    Spectral Information Systems provide a framework to assemble, curate, and serve Spectral data and their associated metadata. This article documents the evolution of the SPECCHIO system, devised to enable long-term usability and data-sharing of field spectroradiometer data. The new capabilities include a modern, web-based client-server architecture, a flexible metadata storage scheme for generic metadata handling, and a rich application programming interface, enabling scientists to directly access Spectral data and metadata from their programming environment of choice. The SPECCHIO system source code has been moved into the open source domain to stimulate contributions from the spectroscopy community while binary distributions, including the SPECCHIO virtual machine, simplify the installation and use of the system for the end-users.

  • Spectral Information system for Australian spectroscopy data
    2013
    Co-Authors: Laurie A. Chisholm, Andreas Hueni, Lola Suarez, Cindy Ong, Natalia Restrepo-coupe
    Abstract:

    Abstract presented at 2013 Fall Meeting, AGU, San Francisco, California, USA, 9-13 Dec. Disciplines Medicine and Health Sciences | Social and Behavioral Sciences Publication Details Chisholm, L. A., Ong, C., Hueni, A., Suarez, L. & Restrepo-Coupe, N. (2013). Spectral Information system for Australian spectroscopy data. Abstract of Atmospheric Sciences 2013 Fall Meeting United States: American Geophysical Union. This conference paper is available at Research Online: http://ro.uow.edu.au/smhpapers/1554

  • GSR - Spectral Information system development for Australia
    2012
    Co-Authors: Andreas Hueni, Laurie A. Chisholm, Lola Suarez, Cindy Ong, Matthew Wyatt
    Abstract:

    Scientific efforts to observe the state of natural systems over time, allowing the prediction of future states, have led to a burgeoning interest for organised storage of Spectral field data and associated metadata, seen as being key to the successful and efficient modeling of such systems. A centralised system for such data established for the Australian remote sensing community aims to standardise storage parameters and metadata thus fostering best practice protocols and collaborative research. Supported by the Australian National Data Service (ANDS), whose aim is to promote connections between data, projects, researchers and institutions, a Spectral Information system based on the already operational SPECCHIO Spectral database system is being augmented to specifically meet the needs of the Australian remote sensing community, and is aligned with the Terrestrial Ecosystem Research Network (TERN) Auscover facility. In this paper we outline the envisaged dataflow and usage of the system as a case study within the context of TERN Auscover. The development of a national Spectral Information system will not only ensure the long-term storage of data but support scientists in data analysis activities, essentially leading to improved repeatability of results, superior reprocessing capabilities, and promotion of best practice.