Support Vector Machines for Land Cover Mapping from Remote Sensor Imagery

2015 ◽  
pp. 265-279 ◽  
Author(s):  
Dee Shi ◽  
Xiaojun Yang
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Remote Sensor ◽  
Vector Machines

scholarly journals Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines

Forests ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 750 ◽  
Author(s):  
Sotiria Fragou ◽  
Kleomenis Kalogeropoulos ◽  
Nikolaos Stathopoulos ◽  
Panagiota Louka ◽  
Prashant K. Srivastava ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Forest Cover ◽  
Spectral Band ◽  
Mixed Forest ◽  
Landsat Tm ◽  
Geographical Information ◽  
Support Vector ◽  
Geoinformation Technologies ◽  
Vector Machines

The rapid advent in geoinformation technologies, such as Earth Observation (EO) and Geographical Information Systems (GIS), has made it possible to observe and monitor the Earth’s environment on variable geographical scales and analyze those changes in both time and space. This study explores the synergistic use of Landsat EO imagery and Support Vector Machines (SVMs) in obtaining Land Use/Land Cover (LULC) mapping and quantifying its spatio-temporal changes for the municipality of Mandra–Idyllia, Attica Region, Greece. The study area is representative of typical Mediterranean landscape in terms of physical structure and coverage of species composition. Landsat TM (Thematic Mapper) images from 1993, 2001 and 2010 were acquired, pre-processed and classified using the SVMs classifier. A total of nine basic classes were established. Eight spectral band ratios were created in order to incorporate them in the initial variables of the image. For validating the classification, in-situ data were collected for each LULC type during several field surveys that were conducted in the area. The overall classification accuracy for 1993, 2001 and 2010 Landsat images was reported as 89.85%, 91.01% and 90.24%, respectively, and with a statistical factor (K) of 0.96, 0.89 and 0.99, respectively. The classification results showed that the total extent of forests within the studied period represents the predominant LULC, despite the intense human presence and its impacts. A marginal change happened in the forest cover from 1993 to 2010, although mixed forest decreased significantly during the studied period. This information is very important for future management of the natural resources in the studied area and for understanding the pressures of the anthropogenic activities on the natural environment. All in all, the present study demonstrated the considerable promise towards the support of geoinformation technologies in sustainable environmental development and prudent resource management.

Assessment of ALOS PALSAR 50 m Orthorectified FBD Data for Regional Land Cover Classification by Support Vector Machines

2011 ◽  
Vol 49 (6) ◽  
pp. 2135-2150 ◽  
Author(s):  
Nicolas Longepe ◽  
Preesan Rakwatin ◽  
Osamu Isoguchi ◽  
Masanobu Shimada ◽  
Yumiko Uryu ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Land Cover Classification ◽  
Support Vector ◽  
Alos Palsar ◽  
Vector Machines ◽  
Regional Land

scholarly journals LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B3 ◽  
pp. 447-452
Author(s):  
M. Zhou ◽  
C. R. Li ◽  
L. Ma ◽  
H. C. Guan
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Prediction Model ◽  
Image Data ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Training Data ◽  
Support Vector ◽  
Vector Machines ◽  
Waveform Lidar

In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

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