Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification

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.

A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks

“…With an increasing amount of data acquired by SAR imaging systems, SAR automatic target recognition (ATR) technology has become one of the research hotspots in the field of image cognition [6], [7]. A growing number of deep neural network (DNN) models have been applied to SAR ATR [8], [9]. Convolutional neural networks, for example, have gradually become the standard model in the field of SAR image processing due to its powerful feature extraction capabilities [10]- [12].…”

Multi-Scale Fused SAR Image Registration Based on Deep Forest

“…Recently, Deep Learning (DL) [34] has made great progress in the field of image processing. Considering that its excellent performance in computer vision [35], deep learning also has been used in SAR image classification [36,37], object recognition of SAR images [38], SAR image segmentation [39], change detection of SAR images [5][6][7], SAR image registration [40], etc. For the SAR image registration, many proposed methods [40][41][42] have demonstrated the availability of deep features.…”

DeepInSAR—A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation

“…Milestone works using Convolutional Neural Network (CNN) have shown their ability to outperform almost all conventional algorithms on different visual related tasks including image restoration. There are also some recent SAR based studies benefited from CNN, including the Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) model [31], which uses an ensemble learning technique to achieve superior prediction on PolSAR images classification task. Ma et al [32] proposed an attention-based graph CNN to improve the SAR segmentation results.…”