Remote sensing analytical geospatial operations directly in the web browser

Masó J., Zabala A., Serral I., Pons X. (2018) Remote sensing analytical geospatial operations directly in the web browser. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 42: 475-482.
Link
Doi: 10.5194/isprs-archives-XLII-4-403-2018

Abstract:

Current map viewers that run on modern web browsers are mainly requesting images generated on the fly in the server side and transferred in pictorial format that they can display (PNG or JPEG). In OGC WMS standard this is done for the whole map view while in WMTS is done per tiles. The user cannot fine tune personalized visualization or data analysis in the client side. Remote sensing data is structured in bands that are visualize individually (manually adjusting contrast), create RGB combinations or present spectral indices. When these operations are not available in map browsers professional are forced to download hundreds of gigabytes of remote sensing imagery to take a good look at the data before deciding if it fits for a purpose. A possible solution is to create a web service that is able to perform these operations on the server side (https://www.sentinel-hub.com). This paper proposes that the server should communicate the data values to the client in a format that the client can directly process using two new additions in HTML5: canvas edition and array buffers. In the client side, the user can interact with a JavaScript interface changing symbolizations and doing some analytical operations without having to request any data again to the server. As a bonus, the user is able to perform queries to the data in a more dynamic way, applying spatial filters, creating histograms, generating animations of a time series or performing complex calculations among bands of the different loaded datasets. © Authors 2018.

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Spatial pattern alterations from JPEG2000 lossy compression of remote sensing images: Massive variogram analysis in high performance computing

Pesquer L., Pons X., Cortes A., Serral I. (2013) Spatial pattern alterations from JPEG2000 lossy compression of remote sensing images: Massive variogram analysis in high performance computing. Journal of Applied Remote Sensing. 7: 0-0.
Link
Doi: 10.1117/1.JRS.7.073595

Abstract:

We evaluate the implications of JPEG2000 lossy compression of remote sensing images for spatial analytical purposes. The main issue is to identify which cases and conditions in geostatistical studies are suitable for using lossy compressed images. For these purposes, an extensive test using Landsat, compact airborne spectrographic imager (CASI), and moderate resolution imaging spectroradiometer (MODIS) image series has been analyzed, through applying and comparing two-dimensional and three-dimensional (spectral and time domains) compression methods with a wide range of compression ratios for several dates, different landscape regions, and spectral bands. Due to the massive test bed and consequently to the high time consuming executions, a parallel solution was specifically developed. Variogram analyses showed that all the compression ratios maintain the variogram shapes, but high compression ratios (>20:1) degrade the spatial patterns of the remote sensing images. These alterations are lower for the three-dimensional compression method, which was a considerable improvement (25%) on the two-dimensional method for large three-dimensional series (CASI, MODIS). However, the two methods behave similarly in the Landsat case. Finally, the parallel solution in a distributed environment demonstrates that high performance computing offers a suitable scientific platform for highly demanding time execution applications, such as geostatistical analyses of remote sensing images. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

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Rubric-Q: Adding quality-related elements to the GEOSS clearinghouse datasets

Zabala A., Riverola A., Serral I., Diaz P., Lush V., Maso J., Pons X., Habermann T. (2013) Rubric-Q: Adding quality-related elements to the GEOSS clearinghouse datasets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6: 1676-1687.
Link
Doi: 10.1109/JSTARS.2013.2259580

Abstract:

Geospatial data have become a crucial input for the scientific community for understanding the environment and developing environmental management policies. The Global Earth Observation System of Systems (GEOSS) Clearinghouse is a catalogue and search engine that provides access to the Earth Observation metadata. However, metadata are often not easily understood by users, especially when presented in ISO XML encoding. Data quality included in the metadata is basic for users to select datasets suitable for them. This work aims to help users to understand the quality information held in metadata records and to provide the results to geospatial users in an understandable and comparable way. Thus, we have developed an enhanced tool (Rubric-Q) for visually assessing the metadata quality information and quantifying the degree of metadata population. Rubric-Q is an extension of a previous NOAA Rubric tool used as a metadata training and improvement instrument. The paper also presents a thorough assessment of the quality information by applying the Rubric-Q to all dataset metadata records available in the GEOSS Clearinghouse. The results reveal that just 8.7% of the datasets have some quality element described in the metadata, 63.4% have some lineage element documented, and merely 1.2% has some usage element described. © 2013 IEEE.

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Emerging data quality from GEOSS integrated clearinghouses

Serral I., Diaz P., Maso J., Pons X. (2012) Emerging data quality from GEOSS integrated clearinghouses. International Geoscience and Remote Sensing Symposium (IGARSS). : 2744-2747.
Link
Doi: 10.1109/IGARSS.2012.6350358

Abstract:

The GEOSS Common Infrastructure (GCI) provides a Clearinghouse (the GEOSS registry and metadata catalogue) and a GEOPortal to discover and visualize EO data in an integrated, standardized and interactive way, as well as broadly use it by the scientific community when dealing with representation and modeling of Earth Systems. EO data sources are ideally elaborated following quality assessment procedures, resulting in quality estimates and other related indicators. The objective of this indicators is to allow users deciding about data fitness for a purpose, but in practice systems providing methods to distribute, show and exploit this producer quality information in a standard and interoperable way are rarely used. This work aims to extract information about data quality from GCI metadata and analyze the obtained results. Additionally, an XML specific tool is able to quick and visually punctuating the metadata that refers to quality. © 2012 IEEE.

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