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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Jiewen Li,Zhicheng Zhao,Yanlan Wu,Jiaqiu Ai,Jun Shi.HOG-VGG: VGG Network with HOG Feature Fusion for High-Precision PolSAR Terrain Classification[J].Journal of Harbin Institute Of Technology(New Series),2024,31(5):1-15.DOI:10.11916/j.issn.1005-9113.2023089.
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HOG-VGG: VGG Network with HOG Feature Fusion for High-Precision PolSAR Terrain Classification
Author NameAffiliation
Jiewen Li School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China 
Zhicheng Zhao Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601,China
School of Artificial Intelligence, Anhui University, Hefei 230601,China 
Yanlan Wu Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601,China
School of Artificial Intelligence, Anhui University, Hefei 230601,China 
Jiaqiu Ai School of Software, Hefei University of Technology, Hefei 230009,China 
Jun Shi School of Software , Hefei University of Technology , Hefei 230009 , China 
Abstract:
This article proposes a VGG network with histogram of oriented gradient (HOG) feature fusion (HOG-VGG) for polarization synthetic aperture radar (PolSAR) image terrain classification. VGG-Net has a strong ability of deep feature extraction, which can fully extract the global deep features of different terrains in PolSAR images, so it is widely used in PolSAR terrain classification. However, VGG-Net ignores the local edge & shape features, resulting in incomplete feature representation of the PolSAR terrains, as a consequence, the terrain classification accuracy is not promising. In fact, edge and shape features play an important role in PolSAR terrain classification. To solve this problem, a new VGG network with HOG feature fusion was specifically proposed for high-precision PolSAR terrain classification. HOG-VGG extracts both the global deep semantic features and the local edge & shape features of the PolSAR terrains, so the terrain feature representation completeness is greatly elevated. Moreover, HOG-VGG optimally fuses the global deep features and the local edge & shape features to achieve the best classification results. The superiority of HOG-VGG is verified on the Flevoland, San Francisco and Oberpfaffenhofen datasets. Experiments show that the proposed HOG-VGG achieves much better PolSAR terrain classification performance, with overall accuracies of 97.54%, 94.63%, and 96.07%, respectively.
Key words:  PolSAR terrain classification  high-precision  HOG-VGG  feature representation completeness elevation  multi-level feature fusion
DOI:10.11916/j.issn.1005-9113.2023089
Clc Number:TN957
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