scholarly journals Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

2014 ◽  
Author(s):  
Daniela I. Moody ◽  
Steven P. Brumby ◽  
Joel C. Rowland ◽  
Garrett L. Altmann ◽  
Amy E. Larson
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Satellite Imagery ◽  
Multispectral Satellite Imagery ◽  
Sparse Approximations

Detection by classification of buildings in multispectral satellite imagery

2016 ◽  
Author(s):  
Tomohiro Ishii ◽  
Edgar Simo-Serra ◽  
Satoshi Iizuka ◽  
Yoshihiko Mochizuki ◽  
Akihiro Sugimoto ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Multispectral Satellite Imagery

scholarly journals Methods of performance evaluation for the supervised classification of satellite imagery in determining land cover classes

2013 ◽  
Vol 40 (2) ◽  
pp. 419-428 ◽  
Author(s):  
Carlos H. Wachholz de Souza ◽  
Erivelto Mercante ◽  
Victor H. R. Prudente ◽  
Diego D.D. Justina
Keyword(s):  
Performance Evaluation ◽  
Land Cover ◽  
Satellite Imagery ◽  
Supervised Classification

Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR

2018 ◽  
Vol 65 ◽  
pp. 12-23 ◽  
Author(s):  
Collins B. Kukunda ◽  
Joaquín Duque-Lazo ◽  
Eduardo González-Ferreiro ◽  
Hauke Thaden ◽  
Christoph Kleinn
Keyword(s):  
Satellite Imagery ◽  
Airborne Lidar ◽  
Ensemble Classification ◽  
Multispectral Satellite Imagery

A new Bayesian ensemble of trees approach for land cover classification of satellite imagery

2014 ◽  
Vol 39 (6) ◽  
pp. 507-520 ◽  
Author(s):  
Reshu Agarwal ◽  
Pritam Ranjan ◽  
Hugh Chipman
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Land Cover Classification

scholarly journals A development of the Enhanced Built-up and Bareness Index (EBBI) based on combination of multi-resolution Landsat 8 and Sentinel 2 MSI images

2021 ◽  
Vol 62 (1) ◽  
pp. 1-9
Author(s):  
Hung Le Trinh ◽  
Ha Thu Thi Le ◽  
Loc Duc Le ◽  
Long Thanh Nguyen ◽  
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Urban Land ◽  
Urban Land Use ◽  
Landsat 8 ◽  
Infrared Band ◽  
Bare Land ◽  
Sentinel 2

Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

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