HOG-VGG:VGG Network with HOG Feature Fusion for High-Precision PolSAR Terrain Classification  

作　　者：Jiewen Li Zhicheng Zhao Yanlan Wu Jiaqiu Ai Jun Shi 

机构地区：[1]School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230009,China [2]Information Materials and Intelligent Sensing Laboratory of Anhui Province,Anhui University,Hefei 230601,China [3]School of Artificial Intelligence,Anhui University,Hefei 230601,China [4]Intelligent Interconnected Systems Laboratory of Anhui Province(Hefei University of Technology),Hefei 230009,China [5]School of Software,Hefei University of Technology,Hefei 230009,China

出　　处：《Journal of Harbin Institute of Technology(New Series)》2024年第5期1-15,共15页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the Fundamental Research Funds for the Central Universities of China(Grant No.PA2023IISL0098);the Hefei Municipal Natural Science Foundation(Grant No.202201);the National Natural Science Foundation of China(Grant No.62071164);the Open Fund of Information Materials and Intelligent Sensing Laboratory of Anhui Province(Anhui University)(Grant No.IMIS202214 and IMIS202102)。

摘　　要：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.

关 键 词：PolSAR terrain classification high⁃precision HOG⁃VGG feature representation completeness elevation multi⁃level feature fusion 

分 类 号：TN957[电子电信—信号与信息处理]