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MODIS Data-based Crop Classification using Selective Hierarchical Classification
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  • Journal title : Korean Journal of Remote Sensing
  • Volume 32, Issue 3,  2016, pp.235-244
  • Publisher : The Korean Society of Remote Sensing
  • DOI : 10.7780/kjrs.2016.32.3.3
 Title & Authors
MODIS Data-based Crop Classification using Selective Hierarchical Classification
Kim, Yeseul; Lee, Kyung-Do; Na, Sang-Il; Hong, Suk-Young; Park, No-Wook; Yoo, Hee Young;
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In large-area crop classification with MODIS data, a mixed pixel problem caused by the low resolution of MODIS data has been one of main issues. To mitigate this problem, this paper proposes a hierarchical classification algorithm that selectively classifies the specific crop class of interest by using their spectral characteristics. This selective classification algorithm can reduce mixed pixel effects between crops and improve classification performance. The methodological developments are illustrated via a case study in Jilin city, China with MODIS Normalized Difference Vegetation Index (NDVI) and Near InfRared (NIR) reflectance datasets. First, paddy fields were extracted from unsupervised classification of NIR reflectance. Non-paddy areas were then classified into corn and bean using time-series NDVI datasets. In the case study result, the proposed classification algorithm showed the best classification performance by selectively classifying crops having similar spectral characteristics, compared with traditional direct supervised classification of time-series NDVI and NIR datasets. Thus, it is expected that the proposed selective hierarchical classification algorithm would be effectively used for producing reliable crop maps.
Crop;MODIS;Hierarchical Classification;NDVI;
 Cited by
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