Development of S-NPP VIIRS global surface type classification map using support vector machines

Zhang, Rui; Huang, Chengquan; Zhan, Xiwu; Jin, Huiran; Song, Xiao-Peng

doi:10.1080/17538947.2017.1315462

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

Development of S-NPP VIIRS global surface type classification map using support vector machines

2017
By Zhang, Rui ; Huang, Chengquan ; Zhan, Xiwu ; ...
Source: International Journal of Digital Earth, 11(2), 212-232

[PDF-2.67 MB]

Details You May Also Like

Details:

Journal Title:

International Journal of Digital Earth
Personal Author:

Zhang, Rui ; Huang, Chengquan ; Zhan, Xiwu ; Jin, Huiran ; Song, Xiao-Peng

Zhang, Rui ; Huang, Chengquan ; Zhan, Xiwu ; Jin, Huiran ; Song, Xiao-Peng Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research)

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) Less -
Description:

With the launch of the Joint Polar Satellite System (JPSS)/Suomi National Polar-orbiting Partnership (S-NPP) satellite in October 2011, many of the terrestrial remote sensing products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), such as the global land cover map, have been inherited and expanded into the JPSS/S-NPP mission using the new Visible Infrared Imaging Radiometer Suite (VIIRS) data. In this study, an improved algorithm including the use of a new classifier support vector machines (SVM) classifier was proposed to produce VIIRS surface type maps. In addition to the new classification algorithm, a new post-processing strategy involving the use of new ancillary data to refine the classification output is implemented. As a result, the new global International Geosphere-Biosphere Programme (IGBP) map based on the 2014 VIIRS surface reflectance data was generated with a 78.5 ± 0.6% overall classification accuracy. The new map was compared to a previously delivered VIIRS surface type map, and to the MODIS land cover product. Validation results including the error matrix, overall accuracy, and the user’s and producer’s accuracy suggest the new global surface type map provides similar classification accuracy compared to the old VIIRS surface type map, with higher accuracy achieved in agricultural types.
Source:

International Journal of Digital Earth, 11(2), 212-232
DOI:

https://doi.org/10.1080/17538947.2017.1315462
ISSN:

1753-8947;1753-8955;
Format:

PDF
Publisher:

Informa UK Limited
Document Type:

Journal Article
Funding:

Grant no. NA14NES4320003
Rights Information:

Accepted Manuscript
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha-512:ea797781462b41eb772ddfb97112adec49020f96a1b6b956a340fec2bf25a6115195ea2a2349a8ded7677999934f1eddfefa797bf9d40da195a3565bf63e2d5f
Download URL:

https://repository.library.noaa.gov/view/noaa/66280/noaa_66280_DS1.pdf
File Type:

No Additional Files

More +

Development of S-NPP VIIRS global surface type classification map using support vector machines

Details:

Supporting Files

You May Also Like