Nonlinear Projective Dictionary Pair Learning for PolSAR Image Classification

Yanqiao Chen,Yangyang Li,Licheng Jiao,Xinghua Chai,Lingling Li,Xu Liu

doi:10.1109/access.2021.3078232

Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification has become a hot research topic in recent years. Sparse representation plays an important role in image processing. However, almost all the existing dictionary learning methods are linear transformation in the original data space, so they cannot capture the nonlinear relationship of the input data. The recently proposed projective dictionary pair learning (DPL) method has acquired good performance in classification result and time consumption. In this paper, we propose the nonlinear projective dictionary pair learning (NDPL) model, which introduced the nonlinear transformation to the DPL model. Our method can adaptively obtain the nonlinear relationship between the elements of input data, and it also has the excellent performance of DPL model. In this paper, we use three PolSAR images to test the performance of our proposed method. Compared with several state-of-the-art methods, our proposed method has obtained promising results in solving the task of PolSAR image classification.

Highlights

Polarimetric synthetic aperture radar (PolSAR) has become one of the most developed technologies [1]
We propose the nonlinear projective dictionary pair learning (NDPL) model, which introduced the nonlinear transformation to the DPL model
We propose a novel method for PolSAR image classification, namely NDPL

Summary

Introduction

Polarimetric synthetic aperture radar (PolSAR) has become one of the most developed technologies [1]. Some other methods were based on the statistical distribution of PolSAR data [10], [11] Such as, Lee etal put forward Wishart distribution [11] can be used to solve the task of PolSAR image classification. Lee etal put forward Wishart distribution [11] can be used to solve the task of PolSAR image classification Most of these methods are based on the complex analysis or physical mechanism of PolSAR data [12], and the further analysis of these methods is so difficult [13]. Dictionary plays a vital part in sparse model, and its function will seriously affect the performance of sparse representation [21]. The latest advances in dictionary learning are not the use of predefined dictionaries, but the dictionaries needed to learn from the training data itself can usually produce good results for many image and video analysis tasks [16], [22], [23]

Methods

Results

Conclusion