NDLI: An approach for quickly labeling land cover types for multiple epochs at globally selected locations

Content Provider	IEEE Xplore Digital Library
Author	Min Feng Chengquan Huang Sexton, J.O. Channan, S. Narasimhan, R. Townshend, J.R.
Copyright Year	2012
Description	Author affiliation: Global Land Cover Facility, Department of Geography, University of Maryland, College Park, 20742, USA (Chengquan Huang; Sexton, J.O.; Channan, S.; Narasimhan, R.; Townshend, J.R.) \|\| State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China (Min Feng)
Abstract	This paper presents a sampling-based land cover labeling approach for efficiently producing high accuracy land cover reference data over large areas. The accuracy of the interpreted land cover data is maximized by associating the sample locations with multiple independent sources of input, such as Landsat images acquired in different seasons and epochs, high-resolution images from Google Maps, and in-situ “ground” photos from web sources. The approach can be adopted for large areas—e.g., countries, continents, or the entire globe—to provide reference data samples for classification model training and validation. An online land cover labeling system has been prototyped at the Global Land Cover Facility (GLCF) at the University of Maryland.
Starting Page	6203
Ending Page	6206
File Size	725798
Page Count	4
File Format	PDF
ISBN	9781467311601
ISSN	21536996
e-ISBN	9781467311595
e-ISBN	9781467311588
DOI	10.1109/IGARSS.2012.6352674
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-22
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Remote sensing Satellites Earth Labeling MODIS Google Databases Land cover reference data Landsat
Content Type	Text
Resource Type	Article

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