A SAR Image Classification Algorithm Based on Multi-Feature Polarimetric Parameters Using FOA and LS-SVM
| Autor: | Ling Tong, Leland Pierce, Kamal Sarabandi, Shiyu Luo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Synthetic aperture radar
0209 industrial biotechnology General Computer Science Computer science 020209 energy ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology FOA Kernel principal component analysis law.invention 020901 industrial engineering & automation law Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering General Materials Science Radar Pixel Contextual image classification General Engineering TK1-9971 Support vector machine classification Electrical engineering. Electronics. Nuclear engineering multi-feature Algorithm Polarimetric SAR image Curse of dimensionality |
| Zdroj: | IEEE Access, Vol 7, Pp 175259-175276 (2019) |
| ISSN: | 2169-3536 |
| Popis: | This paper presents a Synthetic Aperture Radar (SAR) image classification algorithm based on multi-feature using Fruit Fly Optimization Algorithm (FOA) and Least Square Support Vector Machine (LS-SVM). First, pixel-based information derived from three elements of coherency matrix, six parameters obtained by $H/\alpha /A$ decomposition and Freeman decomposition techniques, and three polarimetric parameters including the total receive power (SPAN), pedestal height, and Radar Vegetation Index (RVI), as well as region-based information derived from eight texture parameters obtained by Grey Level Co-occurrence Matrix (GLCM) are combined to use as the features of land cover. Second, Kernel Principal Component Analysis (KPCA) is used to reduce the dimensionality of the multi-feature data derived from the integration of the pixel-based and region-based information. Third, LS-SVM is used as the classifier in this study due to its fast solving speed and desirable classification capability. Since the input parameters of LS-SVM significantly affect the classification performance, we employ FOA to obtain the optimized input parameters. Finally, the experiments on two fully polarimetric SAR images of various crops with a limited number of samples are implemented by the proposed method and other commonly used methods, respectively. The results show that the proposed method can attain better classification performances compared with other methods. |
| Databáze: | OpenAIRE |
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