Abstract
Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided into two categories, namely pixels-based methods and superpixels-based methods. Pixels-based semi-supervised methods are liable to be affected by speckle noises and have a relatively high computational complexity. Superpixels-based methods focus on the superpixels and ignore tiny detail-preserving represented by pixels. In this paper, a Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed. To reduce the effect of speckle noises and preserve the details, FS-SCNN uses a fuzzy superpixels algorithm to segment an image into two parts, superpixels and undetermined pixels. Moreover, the fuzzy superpixels algorithm can also reduce the number of mixed superpixels and improve classification performance. To exploit unlabeled data effectively, we also propose a Similarity-constrained Convolutional Neural Network (SCNN) model to assign pseudo labels to unlabeled data. The final training set consists of the initial labeled data and these pseudo labeled data. Three PolSAR images are used to demonstrate the excellent classification performance of the FS-SCNN method with data of limited labels.
Highlights
The polarimetric synthetic aperture radar (PolSAR) is capable of all-day and all-weather imaging with the penetrability of microwaves
We present the features of pixels of PolSAR images used in the proposed Similarity-constrained Convolutional Neural Network (SCNN) network
A novel fuzzy superpixels based semi-supervised similarity-constrained CNN for the PolSAR image classification method is proposed in this paper
Summary
The polarimetric synthetic aperture radar (PolSAR) is capable of all-day and all-weather imaging with the penetrability of microwaves. Superpixels-based semi-supervised deep learning methods [10,14] take superpixels as the basic unit of input to improve the computation efficiency and reduce the effect of speckle noises using the spatial structure between pixels. Traditional superpixels-based methods may ignore the tiny detail represented by pixels considering that all pixels in any one superpixel are forced to have the same label [9] In both pixels-based and superpixels-based semi-supervised methods, the key issue lies on how to handle unlabeled data effectively. To address the problems mentioned above, a novel Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed in this paper. We propose a Similarity-constrained Convolutional Neural Network (SCNN) model for assigning pseudo labels to unlabeled data.
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