scholarly journals Object-oriented land cover mapping in China national geographical conditions census

10.3990/2.437 ◽  
2016 ◽  
Author(s):  
Liang Zhai ◽  
Huiyong Sang ◽  
Qinghua Qiao
Keyword(s):  
Land Cover ◽  
Object Oriented ◽  
Land Cover Mapping

Object‐oriented classification for urban land cover mapping with ASTER imagery

2007 ◽  
Vol 28 (20) ◽  
pp. 4645-4651 ◽  
Author(s):  
Y. Chen ◽  
P. Shi ◽  
T. Fung ◽  
J. Wang ◽  
X. Li
Keyword(s):  
Land Cover ◽  
Object Oriented ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Urban Land Cover

scholarly journals OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification

2020 ◽  
Vol 12 (6) ◽  
pp. 987 ◽  
Author(s):  
Guangbin Lei ◽  
Ainong Li ◽  
Jinhu Bian ◽  
He Yan ◽  
Lulu Zhang ◽  
...  
Keyword(s):  
Land Cover ◽  
Object Oriented ◽  
Classifier Ensemble ◽  
Land Cover Mapping ◽  
Initial Training ◽  
Sample Collection ◽  
Training Samples ◽  
Multiple Classifier ◽  
Practical Object

Land cover samples are usually the foundation for supervised classification. Unfortunately, for land cover mapping in large areas, only limited samples can be used due to the time-consuming and labor-intensive sample collection. A novel and practical Object-oriented Iterative Classification method based on Multiple Classifiers Ensemble (OIC-MCE) was proposed in this paper. It systematically integrated object-oriented segmentation, Multiple Classifier Ensemble (MCE), and Iterative Classification (IC). In this method, the initial training samples were updated self-adaptively during the iterative processes. Based on these updated training samples, the inconsistent regions (ICR) in the classification results of the MCE method were reclassified to reduce their uncertainty. Three typical case studies in the China-Pakistan Economic Corridor (CPEC) indicate that the overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier. After five iterations, the overall accuracy of the OIC-MCE approach increased by 5.58%–8.38% compared to the accuracy of the traditional MCE method. The spatial distribution of newly added training samples generated by the OIC-MCE approach was relatively uniform. It was confirmed by ten repeated experiments that the OIC-MCE approach has good stability. More importantly, even if the initial sample size reduced by 65%, the quality of the final classification result based on the proposed OIC-MCE approach would not be greatly affected. Therefore, the proposed OIC-MCE approach provides a new solution for land cover mapping with limited samples. Certainly, it is also well suited for land cover mapping with abundant samples.

Optical and Radar Data Analysis for Land Use Land Cover Mapping in Peru

2019 ◽  
Vol 3 (1) ◽  
pp. 14-27
Author(s):  
Barry Haack ◽  
Ron Mahabir
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Window Size ◽  
Radar Data ◽  
Optical Data ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Texture Image ◽  
Site Class ◽  
Spectral Signatures

This analysis determined the best individual band and combinations of various numbers of bands for land use land cover mapping for three sites in Peru. The data included Landsat Thematic Mapper (TM) optical data, PALSAR L-band dual-polarized radar, and derived radar texture images. Spectral signatures were first obtained for each site class and separability between classes determined using divergence measures. Results show that the best single band for analysis was a TM band, which was different for each site. For two of the three sites, the second best band was a radar texture image from a large window size. For all sites the best three bands included two TM bands and a radar texture image. The original PALSAR bands were of limited value. Finally upon further analysis it was determined that no more than six bands were needed for viable classification at each study site.

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