scholarly journals Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

2016 ◽  
Vol 8 (4) ◽  
pp. 304 ◽  
Author(s):  
Yetao Yang ◽  
Yi Wang ◽  
Ke Wu ◽  
Xin Yu
Keyword(s):  
Land Cover ◽  
Rough Set ◽  
Urban Fringe ◽  
Evidential Reasoning ◽  
Fuzzy Rough Set ◽  
Wuhan City

Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition: a fuzzy rough set approach

2017 ◽  
Vol 56 (1) ◽  
pp. 55-84 ◽  
Author(s):  
Sarah Vluymans ◽  
Alberto Fernández ◽  
Yvan Saeys ◽  
Chris Cornelis ◽  
Francisco Herrera
Keyword(s):  
Rough Set ◽  
Imbalanced Data ◽  
Fuzzy Rough Set

scholarly journals Development Of Landsat Cloud Free Image Data For Classification Of Land Cover-Case Study In Dak Lak Province

2019 ◽  
Vol 35 (4) ◽  
Author(s):  
Tran Anh Tuan ◽  
Nguyen Dinh Duong
Keyword(s):  
Land Cover ◽  
Optical Sensors ◽  
Image Data ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Earth Surface ◽  
Dry Seasons

Land cover mapping by optical remote sensing has many obstacles including clouds. Clouds block solar radiation coming to earth surface and reflective radiance from the earth surface to remote optical sensors resulting. Therefore, clouds result no-signal areas in images that cannot be used for study of ground objects. In many cases, thin clouds degrade quality of reflective radiance and some times alter, unexpectedly, spectral reflectance characteristics of ground objects leading to false classification. In this paper, the authors present an algorithm on application of multidate for development of cloud free image. The used image data were received in rainy and dry seasons and by stacking, cloud free images representing rainy and dry seasons were created. These cloud free images can be used further for classification of land cover in rainy and dry seasons. Experiments were conducted with Landsat 8 OLI images with path/row number 124/51 covering Dak Lak province of Vietnam. The results of case study were development of cloud free image data representing rainy and dry seasons allowing separation of evegreen and deciduous forests in the study site.  

scholarly journals A comparison and classification of land use land cover to estimate their effect on environment: case study in Baghdad city

2021 ◽  
Vol 2114 (1) ◽  
pp. 012017
Author(s):  
Bushra A. Ahmed ◽  
Ghaida S. Hadi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Major Change ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Two Cities ◽  
Landsat Satellite

Abstract This study compared and classified of land use and land cover changes by using Remote Sensing (RS) and Geographic Information Systems (GIS) on two cities (Al-Saydiya city and Al-Hurriya) in Baghdad province, capital of Iraq. In this study, Landsat satellite image for 2020 were used for (Land Use/Land Cover) classification. The change in the size of the surface area of each class in the Al-Saydiya city and Al-Hurriya cities was also calculated to estimate their effect on environment. The major change identified, in the study, was in agricultural area in Al-Saydiya city compare with Al-Hurriya city in Baghdad province. The results of the research showed that the percentage of the green area from the total area in Al-Saydiya city is 34.95%, while in Al-Hurriya is 27.53%. Therefore, available results of land use and land cover changes can provide critical input to decision-making of environmental management and planning the future.

scholarly journals LAND USE AND LAND COVER CLASSIFICATION OF SENTINEL 2-A: ST PETERSBURG CASE STUDY

2019 ◽  
Vol XLII-1/W2 ◽  
pp. 13-16 ◽  
Author(s):  
M. Cavur ◽  
H. S. Duzgun ◽  
S. Kemec ◽  
D. C. Demirkan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Remotely Sensed Data ◽  
Vector Machines ◽  
Kappa Value ◽  
Sentinel 2

<p><strong>Abstract.</strong> Land use and land cover (LULC) maps in many areas have been used by companies, government offices, municipalities, and ministries. Accurate classification for LULC using remotely sensed data requires State of Art classification methods. The SNAP free software and ArcGIS Desktop were used for analysis and report. In this study, the optical Sentinel-2 images were used. In order to analyze the data, an object-oriented method was applied: Supported Vector Machines (SVM). An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. The overall classification accuracy of 83,64% with the kappa value of 0.802 was achieved using SVM. The study indicated that of SVM algorithms, the proposed framework on Sentinel-2 imagery results is satisfactory for LULC maps.</p>

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