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Supervised Classification of Fully PolSAR Images Using Active Contour Models
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DOI：10.1109/LGRS.2019.2892524.png)
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Abstract
En 中文
In this letter, we propose a supervised method for the classification of fully polarimetric synthetic aperture radar (PolSAR) images based on active contour models. We use an a priori estimation, obtained from training data, of the complex Wishart distributions of the different types of regions in the image (for instance, water, crops, grass, forest or urban). The information of the Wishart distributions is included in the active contour models to guide the level set evolution. We study the case of two classes and the case of three or more classes separately. We present some experimental results on the synthetic data and real PolSAR images to show the performance of the proposed model. The results are compared with other well-known supervised classification methods, and, for actual PolSAR data, our method shows an overall precision of 94.31% and a kappa coefficient of 0.937.
Keywords:
Active contours
classification
polarimetric synthetic aperture radar (PolSAR) snakes
statistical learning
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