Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application. In the last few years, superpixels are widely used in polarimetric synthetic aperture radar image classification. However, no superpixel algorithm is especially designed for image classification. It is believed that both mixed superpixels and pure superpixels exist in an image. Nevertheless, mixed superpixels have negative effects on classification accuracy. Thus, it is necessary to generate superpixels containing as few mixed superpixels as possible for image classification. In this paper, first, a novel superpixels concept, named fuzzy-superpixels, is proposed for reducing the generation of mixed superpixels. In fuzzy-superpixels, not all pixels are assigned to a corresponding superpixel. We would rather ignore the pixels than assign them to improper superpixels. Second, a new algorithm is proposed to generate fuzzy-superpixels for PolSAR image classification. Three PolSAR images are used to verify the effect of the proposed fuzzy superpixels algorithm. Experimental results demonstrate the superiority of the proposed fuzzy-superpixels algorithm over several state-of-the-art superpixels algorithms.

Terrain classifications is an important topic in polarimetric synthetic aperture radar (PolSAR) image processing and interpretation. A novel PolSAR classification method based on three-channel convolutional neural network (Tc-CNN) is proposed and this method can effectively take the advantage of unlabeled samples to improve the performance of classification with a small number of labeled samples. Several strategies are included in the proposed method. (1) In order to take the advantage of unlabeled samples, a data enhancement method based on neighborhood nearest neighbor propagation (N3P) method is proposed to enlarge the number of labeled samples. (2) To increase the role of central pixel in CNN classification based on pixel, a spatial weighted method is proposed to increase the weight of central pixel features and weak the weight of other types of pixel features. (3) A specific deep model for PolSAR image classification (named as Tc-CNN) is proposed, which can obtain more scale and deep polarization information to improve the classification results. Experimental results show that the proposed method achieves a much better performance than existing classification methods when the number of labeled samples is few. Index Terms-convolutional neural network (CNN); polarimetric SAR; terrain classification; three-channel convolutional neural network (Tc-CNN)

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