A novel semicoupled projective dictionary pair learning method for PolSAR image classification

Research output: Contribution to journalArticle

Authors

  • Yanqiao Chen
  • Licheng Jiao
  • Yangyang Li
  • Lingling Li
  • Dan Zhang
  • Bo Ren

Colleges, School and Institutes

External organisations

  • Xidian University
  • Extreme Robotics Lab

Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification plays an important role in remote sensing image processing. In recent years, stacked auto-encoder (SAE) has obtained a series of excellent results in PolSAR image classification. The recently proposed projective dictionary pair learning (DPL) model takes both accuracy and time consumption into consideration, and another recently proposed semicoupled dictionary learning (SCDL) model gives a new way to fit different features. Based on the SAE, DPL, and SCDL models, we propose a novel semicoupled projective DPL method with SAE (SAE-SDPL) for PolSAR image classification. Our method can get the classification result efficiently and correctly and meanwhile giving a new method to fit different features. In this paper, three PolSAR images are used to test the performance of SAE-SDPL. Compared with some state-of-the-art methods, our method obtains excellent results in PolSAR image classification.

Details

Original languageEnglish
Article number8509609
Pages (from-to)2407-2418
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume57
Issue number4
Early online date31 Oct 2018
Publication statusPublished - 1 Apr 2019

Keywords

  • Polarimetric synthetic aperture radar (PolSAR), projective dictionary pair learning (DPL), semicoupled dictionary learning (SCDL), semicoupled projective DPL (SDPL), stacked auto-encoder (SAE)