Polarimetric SAR Sea Ice Classification Based on Target Decompositional Features
Feature extraction and its selection are one of the most important steps in the SAR sea ice classification. The key to improve the classification accuracy is to select effective features and to construct the feature space that effectively expresses the type of ground objects. For this purpose, a ful...
| Main Authors: | , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | Chinese |
| Published: |
Surveying and Mapping Press
2018
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| Subjects: | |
| Online Access: | https://doi.org/10.11947/j.AGCS.2018.20170551 https://doaj.org/article/d5231f0f20b74a1994f9e94d6b99122a |
| Summary: | Feature extraction and its selection are one of the most important steps in the SAR sea ice classification. The key to improve the classification accuracy is to select effective features and to construct the feature space that effectively expresses the type of ground objects. For this purpose, a full polarimetric SAR sea ice classification algorithm based on target decomposition features is proposed in this paper. First of all, multilook process and filter operation are preformed to full-pol SAR data and result in coherency matrix. Secondly, in order to construct the feature space, target decomposition on coherency matrix is employed to extract related scattering feature parameters. Thirdly, after analysis of statistical correlation about extracting features, PCA feature reduction operation is carried out on those higher relevant features for the purpose of optimizing the combination of features. Finally, a BP neural network-based classification algorithm is designed to classify sea ice, and the optimization of the feature vector as input layer, the class of sea ice as output layer. In experiment, the central Greenland area is regard as the research area and L-band ALOS PALSAR full polarimetric data are utilized as experimental data. Through the qualitative and quantitative analysis for the proposed and comparing algorithms, it can be found that the feature space built up can efficiently distinguish various sea ices. Furthermore, by analyzing the performance of sea ice classification results with different feature combination, we can conclude that the features of the target decomposition based on scattering model can provide a better capability to identify water and sea ices compared to H/α/A decomposition based on eigenvalue. |
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