| 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. |
|
| Author Name | Affiliation | | 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 |
| Fund: |
