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

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A novel semicoupled projective dictionary pair learning method for PolSAR image classification. / Chen, Yanqiao; Jiao, Licheng; Li, Yangyang; Li, Lingling; Zhang, Dan; Ren, Bo; Marturi, Naresh.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 4, 8509609, 01.04.2019, p. 2407-2418.

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Chen, Yanqiao ; Jiao, Licheng ; Li, Yangyang ; Li, Lingling ; Zhang, Dan ; Ren, Bo ; Marturi, Naresh. / A novel semicoupled projective dictionary pair learning method for PolSAR image classification. In: IEEE Transactions on Geoscience and Remote Sensing. 2019 ; Vol. 57, No. 4. pp. 2407-2418.

Bibtex

@article{150cdfe38a054ec893761ed990045125,
title = "A novel semicoupled projective dictionary pair learning method for PolSAR image classification",
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.",
keywords = "Polarimetric synthetic aperture radar (PolSAR), projective dictionary pair learning (DPL), semicoupled dictionary learning (SCDL), semicoupled projective DPL (SDPL), stacked auto-encoder (SAE)",
author = "Yanqiao Chen and Licheng Jiao and Yangyang Li and Lingling Li and Dan Zhang and Bo Ren and Naresh Marturi",
year = "2019",
month = apr,
day = "1",
doi = "10.1109/TGRS.2018.2873302",
language = "English",
volume = "57",
pages = "2407--2418",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "4",

}

RIS

TY - JOUR

T1 - A novel semicoupled projective dictionary pair learning method for PolSAR image classification

AU - Chen, Yanqiao

AU - Jiao, Licheng

AU - Li, Yangyang

AU - Li, Lingling

AU - Zhang, Dan

AU - Ren, Bo

AU - Marturi, Naresh

PY - 2019/4/1

Y1 - 2019/4/1

N2 - 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.

AB - 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.

KW - Polarimetric synthetic aperture radar (PolSAR)

KW - projective dictionary pair learning (DPL)

KW - semicoupled dictionary learning (SCDL)

KW - semicoupled projective DPL (SDPL)

KW - stacked auto-encoder (SAE)

UR - http://www.scopus.com/inward/record.url?scp=85055679909&partnerID=8YFLogxK

U2 - 10.1109/TGRS.2018.2873302

DO - 10.1109/TGRS.2018.2873302

M3 - Article

AN - SCOPUS:85055679909

VL - 57

SP - 2407

EP - 2418

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 4

M1 - 8509609

ER -