Comparison of land cover classification methods based on single-temporal MODIS data

2008 ◽  
Author(s):  
Tao Han ◽  
Xiaotao Xu ◽  
Yaohui Li ◽  
Yaowen Xie
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Classification Methods ◽  
Modis Data

Land cover classification for the monitoring of winter grain crops within the Kyiv oblast and Mykolaiv oblast (Ukraine) with the use of MODIS data

2009 ◽  
Vol 15 (5) ◽  
pp. 16-23
Author(s):  
O.I. Sakhatsky ◽  
◽  
G.M. Zholobak ◽  
A.A. Makarova ◽  
O.A. Apostolov ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Grain Crops ◽  
Modis Data

Land cover classification in Kii Peninsula of Japan using Terra/MODIS data

2003 ◽  
Author(s):  
Cen Yi ◽  
Liangpei Zhang ◽  
Shinobu Furumi ◽  
Kanako Muramatsu ◽  
Noboru Fujiwara
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Kii Peninsula ◽  
Modis Data ◽  
Terra Modis

scholarly journals Refining Land-Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very-High-Resolution Remote Sensing Images

2018 ◽  
Author(s):  
Cui Guo Qing ◽  
Lv Zhi Yong ◽  
Li GuangFei ◽  
Jón Atli Benediktsson ◽  
Lu Yu Dong
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Classification ◽  
Majority Voting ◽  
Classification Methods ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Central Pixel ◽  
Voting Strategy ◽  
Very High

Land-cover classification that uses very-high-resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, there is still room for improvements in the accuracy and usability of classification systems. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed for improving the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. 1) An adaptive region is generated by extending gradually the region around a central pixel based on two predefined parameters (T1 and T2) in order to utilize the spatial feature of ground targets in a VHR image. 2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting (AMV). Each initial classified map is refined in this manner pixel by pixel. 3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated through two remote sensing images with high spatial resolutions of 1.0 and 1.3 m, respectively. Compared with the classical majority voting method and a relatively new post-processing method called general post-classification framework, the proposed D-AMVS can achieve a land-cover classification map with less noise and higher classification accuracies.

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