scholarly journals LAND COVER CLASSIFICATION OF ALOS PALSAR DATA USING SUPPORT VECTOR MACHINE

2014 ◽  
Vol 10 (1) ◽  
Author(s):  
Katmoko Ari Sambodo ◽  
Novie Indriasari
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Feature Space ◽  
Land Cover Classification ◽  
Support Vector ◽  
Rubber Plantation ◽  
Alos Palsar ◽  
Training Samples ◽  
Separating Hyperplane

Land cover classification is  one  of  the  extensive  used  applications in  the  field  of remote sensing. Recently, Synthetic Aperture Radar (SAR) data has become an increasing popular data source because  its  capability  to  penetrate  through  clouds,  haze,  and  smoke.  This  study  showed  on  an alternative  method  for  land  cover  classification  of  ALOS-PALSAR  data  using  Support  Vector Machine (SVM) classifier. SVM discriminates two classes by fitting an optimal separating hyperplane to the training data in a multidimensional feature space, by using only the closest training samples. In order  to  minimize  the  presence  of  outliers  in  the  training  samples  and  to  increase  inter-class separabilities,  prior  to  classification,  a  training  sample  selection  and  evaluation  technique  by identifying its position in a horizontal vertical–vertical horizontal polarization (HV-HH) feature space was applied. The effectiveness of our method was demonstrated using ALOS PALSAR data (25 m mosaic, dual polarization) acquired in Jambi and South Sumatra, Indonesia. There were nine different classes  discriminated:  forest,  rubber  plantation,  mangrove  &  shrubs  with  trees,  oilpalm  &  coconut, shrubs,  cropland,  bare  soil,  settlement,  and  water.  Overall  accuracy  of  87.79%  was  obtained,  with producer’s accuracies for forest, rubber plantation, mangrove & shrubs with trees, cropland, and water class were greater than 92%.

Intelligent Bearing Diagnostics Using Wavelet Support Vector Machine

2014 ◽  
Vol 493 ◽  
pp. 337-342 ◽  
Author(s):  
Achmad Widodo ◽  
I. Haryanto ◽  
T. Prahasto
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
Intelligent System ◽  
Feature Space ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Fault Diagnostics ◽  
Standard Data ◽  
Rolling Element

This paper deals with implementation of intelligent system for fault diagnostics of rolling element bearing. In this work, the proposed intelligent system was basically created using support vector machine (SVM) due to its excellent performance in classification task. Moreover, SVM was modified by introducing wavelet function as kernel for mapping input data into feature space. Input data were vibration signals acquired from bearings through standard data acquisition process. Statistical features were then calculated from bearing signals, and extraction of salient features was conducted using component analysis. Results of fault diagnostics are shown by observing classification of bearing conditions which gives plausible accuracy in testing of the proposed system.

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

Impact of Training Set Size on Object-Based Land Cover Classification

2014 ◽  
Vol 5 (3) ◽  
pp. 49-67 ◽  
Author(s):  
Gerhard Myburgh ◽  
Adriaan van Niekerk
Keyword(s):  
Land Cover ◽  
Classification Tree ◽  
Land Cover Classification ◽  
Training Sample ◽  
Support Vector ◽  
Training Set ◽  
Training Sample Size ◽  
Set Size ◽  
Object Based ◽  
Training Samples

Supervised classifiers are commonly employed in remote sensing to extract land cover information, but various factors affect their accuracy. The number of available training samples, in particular, is known to have a significant impact on classification accuracies. Obtaining a sufficient number of samples is, however, not always practical. The support vector machine (SVM) is a supervised classifier known to perform well with limited training samples and has been compared favourably to other classifiers for various problems in pixel-based land cover classification. Very little research on training-sample size and classifier performance has been done in a geographical object-based image analysis (GEOBIA) environment. This paper compares the performance of SVM, nearest neighbour (NN) and maximum likelihood (ML) classifiers in a GEOBIA environment, with a focus on the influence of training-set size. Training-set sizes ranging from 4-20 per land cover class were tested. Classification tree analysis (CTA) was used for feature selection. The results indicate that the performance of all the classifiers improved significantly as the size of the training set increased. The ML classifier performed poorly when few (<10 per class) training samples were used and the NN classifier performed poorly compared to SVM throughout the experiment. SVM was the superior classifier for all training-set sizes although ML achieved competitive results for sets of 12 or more training areas per class.

Detection and Classification of Embung Land Cover using Support Vector Machine

2021 ◽  
Author(s):  
Ahmad Syarif Hidayat ◽  
Fatwa Ramdani ◽  
Fitra Bachtiar
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Support Vector
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