Remote Sensing Image Classification by PSONN
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| Title | Remote Sensing Image Classification by PSONN |
| Authors | |
| Abstract | Polarimetric synthetic aperture radar has attracted the research interest among numerous scholars and researchers. It plays an important role in either the military or the domestic fields. In this study, we proposed a novel Polarimetric SAR image classification method. We first extract the feature sets including span image, the H/A/α decomposition, and the gray-level co-occurrence matrix based texture features. Afterwards, we used an artificial neural network to construct the classifier and chose the particle swarm optimization method as the training algorithm. The experiments used the San Francisco area as the test data, and compared our PSONN method with traditional IPNN method and SVM method. The results show that our method achieves the best classification accuracy as 96.55%, meanwhile, IPNN and SVM only achieved 96.13% and 96.43% classification accuracy, respectively. Therefore, our method is effective. |
| Publisher | World Science Publisher |
| Date | 2012-06-06 |
| Source | 2167-6372 |
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