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Unsupervised Classification of Fully Polarimetric SAR Image Based on Polarimetric Features and Spatial Features

Journal from gdlhub / 2016-11-11 07:13:35
Oleh : Xiaorong Xue, Liping Di, Liying Guo, Li Lin, Telkomnika
Dibuat : 2016-09-01, dengan 1 file

Keyword : Polarimetric SAR; polarimetric feature; gray level co–occurrence matrix; image classification
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/4403

Polarimetric SAR(PolSAR) has played more and more important roles in earth observation. Polarimetric SAR image classification is one of the key problems in the PolSAR image interpretation. In this paper, based on the scattering properties of fully polarimetric SAR data, combing the statistical characteristics and neighborhood information, an efficient unsupervised method of fully polarimetric SAR image classification is proposed. In the method, polarimetric scattering characteristics of fully polarimetric SAR image is used, and in the denoised total power image of polarimetric SAR, SPAN (the total polarimetric power), the texture features of gray level co–occurrence matrix are extracted at the same time. Finally, the polarimetric information and texture information are combined for fully polarimetric SAR Image classification with clustering algorithm. The experimental results show that better classification results can be obtained in the Radarsat-2 data with the proposed method.

Deskripsi Alternatif :

Polarimetric SAR(PolSAR) has played more and more important roles in earth observation. Polarimetric SAR image classification is one of the key problems in the PolSAR image interpretation. In this paper, based on the scattering properties of fully polarimetric SAR data, combing the statistical characteristics and neighborhood information, an efficient unsupervised method of fully polarimetric SAR image classification is proposed. In the method, polarimetric scattering characteristics of fully polarimetric SAR image is used, and in the denoised total power image of polarimetric SAR, SPAN (the total polarimetric power), the texture features of gray level co–occurrence matrix are extracted at the same time. Finally, the polarimetric information and texture information are combined for fully polarimetric SAR Image classification with clustering algorithm. The experimental results show that better classification results can be obtained in the Radarsat-2 data with the proposed method.

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