An efficient method of classification of fully polarimetric SAR images
/ 0
- Details
- Category: Information technologies, systems analysis and administration
- Last Updated on 04 February 2016
- Published on 04 February 2016
- Hits: 4207
Authors:
Xiaorong Xue, Anyang Normal University, Anyang, China
Wei Wang, Institute of Disaster Prevention, Beijing, China
Hongfu Wang, Anyang Normal University, Anyang, China
Fang Xiang, Anyang Normal University, Anyang, China
Abstract:
Purpose. Synthetic Aperture Radar (SAR) can be used to acquire high-resolution images of ground targets during night as well as day or in good weather as well as inclement weather, so it plays an important role in the national economy and defense. However, according to the characteristics of SAR imaging mechanisms, a geometric distortion and a form of multiplicative noise, known as coherent speckle, generally corrupt the resulting image. SAR image classification is the foundation of SAR image interpretation. For the existing of speckle, traditional image classification technologies cannot work well. In this paper, in order to further improve the effect of polarimetric SAR image classification, an efficient classification method of fully polarimetric SAR image that is based on polarimetric features, the scattering intensity information, and Fuzzy C-Means (FCM) Algorithm is proposed.
Methodology. Combining the scattering properties of fully polarimetric SAR image with the scattering intensity information, the total scattering power, based on H/α/A/SPAN(H, Entropy; α, Scattering angle; A, Anisotropy degree; SPAN, the total power of polarization), we obtained the initial classification result of polarimetric SAR image. Then with FCM Algorithm, the result of the polarimetric SAR image classification was achieved.
Findings. The experimental results show that the proposed method is superior to the traditional methods of fully polarimetric SAR images classification.
Originality. The proposed method not only considers the scattering properties of fully polarimetric SAR data but also combines the statistical characteristics information. The proposed method provides good result of classification of polarimetric SAR image and, to some extent, keeps the scattering properties.
Practical value. The experiments have proved that the proposed algorithm can keep the texture and details of SAR image better, can give better classification result to the traditional classification methods of fully polarimetric SAR image. The proposed method is useful in SAR image interpretation.
References:
-
J.S. Lee and E. Pottier (2008), Polarimetric Radar Imaging, CRC Press, Boca Raton, FL, USA.
-
Yaqiu Jin and Feng Xu (2013), Polarimetric Scattering and SAR Information Retrieval, Wiley-IEEE Press, USA.
-
Stefan Uhlmann and Serkan Kiranyaz (2014), “Integrating color features in polarimetric SAR image classification”, IEEE Transactions on Geoscience and Remote Sensing, vol.52, no.4, pp. 2197−2216.
-
Lang Fengkai, Yang Jie, Zhao Ling, Zhang Jing and Li Deren (2012), “Polarimetric SAR Data classification with Freeman entropy and anisotropy analysis”, Journal of Surveying and Mapping, vol.9, no. 4, pp. 556−562.
-
Formont P., Pascal F., Vasile G., Ovarlez, J. and Ferro-Famil, L. (2011), “Statistical classification for heterogeneous polarimetric SAR images”, IEEE Journal of Selected Topics in Signal Processing, vol.5, no.3, pp. 567−576.
-
Xiaoshuang Ma, Huanfeng Shen, Jie Yang, Liangpei Zhang and Pingxiang Li. (2014), “Polarimetric-spatial classification of SAR images based on the fusion of multiple classifiers”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, no.3, pp. 961−971.
-
Ciro D’Elia, Simona Ruscino, Maurizio Abbate, Bruno Aiazzi, Stefano Baronti and Luciano Alparone (2014), “SAR image classification through information-theoretic textural features, MRF segmentation, and object-learning vector quantization”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, no.4, pp. 1116−1126.
-
Peter Yu, A.K. Qin, David A. Clausi (2012), “Unsupervised Polarimetric SAR Image Segmentation and Classification Using Region Growing with Edge Penalty”, IEEE Transactions on Geoscience and Remote Sensing, vol.50, no.4, pp. 1302−1317.
-
Shuiping Gou, Xin Qiao, Xiangrong Zhang, Weifang Wang and Fangfang Du (2014), “Eigenvalue Analysis-Based Approach for POL-SAR Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, vol.52, no.2, pp. 805−818.
Chu He, Tong Zhuo, Dan Ou, Ming Liu, Mingsheng Liao (2014), “Nonlinear compressed sensing-based LDA topic model for polarimetric SAR image classification”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, no.3, pp. 972−982.
 2015_05_xue
|
|
 2016-02-03  615.24 KB  1045 |
|
Archive