TY - GEN
T1 - Hybrid SVM and SVSA method for classification of remote sensing images
AU - Kaya, G. Taşkin
AU - Ersoy, O. K.
AU - Kamaşak, M. E.
PY - 2010
Y1 - 2010
N2 - A linear support vector machine (LSVM) is based on determining an optimum hyperplane that separates the data into two classes with the maximum margin. The LSVM typically has high classification accuracy for linearly separable data. However, for nonlinearly separable data, it usually has poor performance. For this type of data, the Support Vector Selection and Adaptation (SVSA) method was developed, but its classification accuracy is not very high for linearly separable data in comparison to LSVM. In this paper, we present a new classifier that combines the LSVM with the SVSA, to be called the Hybrid SVM and SVSA method (HSVSA), for classification of both linearly and nonlinearly separable data and remote sensing images as well. The experimental results show that the HSVSA has higher classification accuracy than the traditional LSVM, the nonlinear SVM (NSVM) with the radial basis kernel, and the previous SVSA.
AB - A linear support vector machine (LSVM) is based on determining an optimum hyperplane that separates the data into two classes with the maximum margin. The LSVM typically has high classification accuracy for linearly separable data. However, for nonlinearly separable data, it usually has poor performance. For this type of data, the Support Vector Selection and Adaptation (SVSA) method was developed, but its classification accuracy is not very high for linearly separable data in comparison to LSVM. In this paper, we present a new classifier that combines the LSVM with the SVSA, to be called the Hybrid SVM and SVSA method (HSVSA), for classification of both linearly and nonlinearly separable data and remote sensing images as well. The experimental results show that the HSVSA has higher classification accuracy than the traditional LSVM, the nonlinear SVM (NSVM) with the radial basis kernel, and the previous SVSA.
KW - Hybrid SVM and SVSA
KW - Support Vector Selection and Adaptation
KW - Support vector machines
UR - https://www.scopus.com/pages/publications/78650894369
U2 - 10.1109/IGARSS.2010.5649062
DO - 10.1109/IGARSS.2010.5649062
M3 - Conference contribution
AN - SCOPUS:78650894369
SN - 9781424495658
SN - 9781424495665
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 2828
EP - 2831
BT - 2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Y2 - 25 July 2010 through 30 July 2010
ER -