To improve polarimetric synthetic aperture radar (PolSAR) imagery segmentation accuracy, a modified non-negative matrix factorization algorithm based on the support vector machine is proposed. Focusing on PolSAR remote sensing images, the modified non-negative matrix factorization algorithm with the neurodynamic optimization achieves the image feature extraction. Compared with basic features, such as the basic backscatter coefficient, structuring more targeted localization non-negative character fits better for the physical significance of remote sensing images. Furthermore, based on the new constructive features, a support vector machine is employed for remote sensing image classification, which remedies the small sample training problem. Simulation results on PolSAR image classification substantiate the effectiveness of the proposed approach.
|Number of pages||8|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2014|
- Image segmentation
- Non-negative matrix factorization
- Support vector machine