ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-4, 49-52, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-4/49/2016/
doi:10.5194/isprs-annals-III-4-49-2016
 
03 Jun 2016
ENHANCED DATA DISCOVERABILITY FOR IN SITU HYPERSPECTRAL DATASETS
B. Rasaiah1, C. Bellman2, R.D. Hewson2, S. D. Jones2, and T. J. Malthus3 1University Corporation for Atmospheric Research, Tuscaloosa, AL 35401, USA
2Centre for Remote Sensing, RMIT University Melbourne, VIC 3001, Australia
3CSIRO Land and Water, Canberra, ACT 2601, Australia
Keywords: Databases, Data mining, Hyperspectral, Metadata, Calibration, Data Quality, Interoperability, Standards Abstract. Field spectroscopic metadata is a central component in the quality assurance, reliability, and discoverability of hyperspectral data and the products derived from it. Cataloguing, mining, and interoperability of these datasets rely upon the robustness of metadata protocols for field spectroscopy, and on the software architecture to support the exchange of these datasets. Currently no standard for in situ spectroscopy data or metadata protocols exist. This inhibits the effective sharing of growing volumes of in situ spectroscopy datasets, to exploit the benefits of integrating with the evolving range of data sharing platforms. A core metadataset for field spectroscopy was introduced by Rasaiah et al., (2011-2015) with extended support for specific applications. This paper presents a prototype model for an OGC and ISO compliant platform-independent metadata discovery service aligned to the specific requirements of field spectroscopy. In this study, a proof-of-concept metadata catalogue has been described and deployed in a cloud-based architecture as a demonstration of an operationalized field spectroscopy metadata standard and web-based discovery service.
Conference paper (PDF, 981 KB)


Citation: Rasaiah, B., Bellman, C., Hewson, R. D., Jones, S. D., and Malthus, T. J.: ENHANCED DATA DISCOVERABILITY FOR IN SITU HYPERSPECTRAL DATASETS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-4, 49-52, doi:10.5194/isprs-annals-III-4-49-2016, 2016.

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