Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery

2011 ◽  
Vol 50 (12) ◽  
pp. 126201 ◽  
Author(s):  
Liangpei Zhang
Keyword(s):  
Land Cover ◽  
Mixture Model ◽  
Hyperspectral Imagery ◽  
Super Resolution ◽  
Land Cover Mapping ◽  
Spectral Mixture

scholarly journals Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering

2018 ◽  
Vol 10 (8) ◽  
pp. 1212 ◽  
Author(s):  
Xiaohong Yang ◽  
Zhong Xie ◽  
Feng Ling ◽  
Xiaodong Li ◽  
Yihang Zhang ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Pattern ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Land Cover Mapping ◽  
Fuzzy C Means Clustering ◽  
Land Cover Map ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Land Cover Maps

Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.

scholarly journals Super-resolution land cover mapping by deep learning

2019 ◽  
Vol 10 (6) ◽  
pp. 598-606 ◽  
Author(s):  
Feng Ling ◽  
Giles M. Foody
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Super Resolution ◽  
Land Cover Mapping

Geostatistical Solutions for Super-Resolution Land Cover Mapping

2008 ◽  
Vol 46 (1) ◽  
pp. 272-283 ◽  
Author(s):  
Alexandre Boucher ◽  
Phaedon C. Kyriakidis ◽  
Collin Cronkite-Ratcliff
Keyword(s):  
Land Cover ◽  
Super Resolution ◽  
Land Cover Mapping
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