Nonlinear Manifold Learning Integrated with Fully Convolutional Networks for PolSAR Image Classification

Chu He,Mingxia Tu,Dehui Xiong,Mingsheng Liao

doi:10.3390/rs12040655

Abstract

Synthetic Aperture Rradar (SAR) provides rich ground information for remote sensing survey and can be used all time and in all weather conditions. Polarimetric SAR (PolSAR) can further reveal surface scattering difference and improve radar’s application ability. Most existing classification methods for PolSAR imagery are based on manual features, such methods with fixed pattern has poor data adaptability and low feature utilization, if directly input to the classifier. Therefore, combining PolSAR data characteristics and deep network with auto-feature learning ability forms a new breakthrough direction. In fact, feature learning of deep network is to realize function approximation from data to label, through multi-layer accumulation, but finite layers limit the network’s mapping ability. According to manifold hypothesis, high-dimensional data exists in potential low-dimensional manifold and different types of data locates in different manifolds. Manifold learning can model core variables of the target, and separate different data’s manifold as much as possible, so as to complete data classification better. Therefore, taking manifold hypothesis as a starting point, nonlinear manifold learning integrated with fully convolutional networks for PolSAR image classification method is proposed in this paper. Firstly, high-dimensional polarized features are extracted based on scattering matrix and coherence matrix of original PolSAR data, whose compact representation is mined by manifold learning. Meanwhile, drawing on transfer learning, pre-trained Fully Convolutional Networks (FCN) model is utilized to learn deep spatial features of PolSAR imagery. Considering complementary advantages, weighted strategy is adopted to embed manifold representation into deep spatial features, which are input into support vector machine (SVM) classifier for final classification. A series of experiments on three PolSAR datasets have verified effectiveness and superiority of the proposed classification algorithm.

Highlights

Synthetic Aperture Radar (SAR) is a typical representative of remote sensing technology, and it has both range and azimuth resolution
In view of the two main problems aforementioned, that is, how to mine deep features with strong adaptability and high utilization and nonlinear learning for high-dimensional polarized feature, this paper proposed a new method, namely nonlinear manifold learning integrated with fully convolutional networks for Polarimetric SAR (PolSAR) image classification
It can be noted that T3-Fully Convolutional Networks (FCN) (OA 96.34%) has achieved the highest accuracy among all the comparison algorithms, which has exceeded t-distributed Stochastic Neighbor Embedding (TSNE)-FCN

Summary

Introduction

Synthetic Aperture Radar (SAR) is a typical representative of remote sensing technology, and it has both range and azimuth resolution. Compared with common remote sensing data, Polarimetric SAR (PolSAR) data stores ground objects’ scattering echo in sinclair or scattering matrix, which can describe land cover more effectively. For PolSAR data, there are mainly statistical features and polarization features of target decomposition based on original data. Considering that different types of data have similar canonical covariance matrix, Novak et al [4] designed a filter classifier based on whitening transformation. Another main manual feature is based on target decomposition, whose purpose is to analyze target’s scattering mechanism with appropriate physical constraints, such as Pauli decomposition [5], Freeman decomposition [6]. Different target decompositions have their own emphases and advantages, and are widely used in ground object classification

Methods

Results

Discussion

Conclusion