Computer Science ›› 2021, Vol. 48 ›› Issue (9): 140-145.doi: 10.11896/jsjkx.200800002

• Computer Graphics & Multimedia • Previous Articles     Next Articles

High-resolution Image Building Target Detection Based on Edge Feature Fusion

HE Xiao-hui1, QIU Fang-bing2, CHENG Xi-jie2, TIAN Zhi-hui1, ZHOU Guang-sheng3   

  1. 1 School of Earth Science and Technology,Zhengzhou University,Zhengzhou 450052,China
    2 School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China
    3 Joint Laboratory of Eco-Meteorology,Chinese Academy of Meteorological Sciences,Zhengzhou University,Zhengzhou 450052,China
  • Received:2020-08-01 Revised:2020-09-10 Online:2021-09-15 Published:2021-09-10
  • About author:HE Xiao-hui,born in 1978,professor.Her main research interests include artificial intelligence,computer vision,remote sensing image processing,and data mining.
  • Supported by:
    Second Tibetan Plateau Scientific Expedition and Research(STEP) Program(2019QZKK0106)

Abstract: High-resolution remote sensing image building target detection has a wide range of application value in territorial planning,geographic monitoring,smart city and other fields.However,due to the complex background of remote sensing images,some detailed features of building targets are less distinguishable from the background.During the task,it is prone to problems such as distortion and missing of the building outline.Aiming at this problem,an adaptive weighted edge feature fusion network (VAF-Net) is designed.This method is aimed at remote sensing image building detection tasks,expands the classic codec network U-Net network,and makes up for the lack of detailed features in basic network learning through the fusion of RGB feature maps and edge feature maps.At the same time,relying on network learning to automatically update the fusion weight,adaptive weighted fusion can be achieved,and the complementary information of different features can be full made use of.This method is tested on the Massachusetts Buildingsdata set,and its accuracy,recall and F1-score reach 82.1%,82.5% and 82.3%,respectively.The comprehensive index F1-score increases by about 6% compared to the basic network.VAF-Net effectively improves the perfor-mance of the codec network for high-resolution image building target detection tasks,and has good practical value.

Key words: Edge feature, Feature fusion, Neural network, Target detection, U-Net

CLC Number: 

  • TP391.4
[1]YANG Z,MU X D,WANG S Y.Scene classficition of remote sensing images based on multiscale features fusion[J].Optics and Precision Engineering,2018,26(12):3099-3107.
[2]LONG J,SHELHAMER E,DARRELL T.Fully Convolutional Networks for Semantic Segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,39(4):640-651.
[3]XU Z J,YANG X B,HE L M,et al.Multiscale remote sensing semantic segmentation Network[J].Computer Engineering and Applications,2020,56(21):210-217.
[4]RONNEBERGER O,FISCHER P,BROX T.U-Net:Convolu-tional Networks for Biomedical Image Segmentation[J].arXiv:1505.04597,2015.
[5]LIU H,LUO J C,HUANG B,et al.DE-Net:Deep EncodingNetwork for Building Extraction from High-Resolution Remote Sensing Imagery[J].Remote Sensing,2019,11(20):2380.
[6]XU Z H,LIU Y,QUAN J C,et al.Buildings egmentation of remote sensing images based on VGG16 pre-encoding[J].Science Technology and Engineering,2019,19(17):250-255.
[7]REN X L,WANG Y P,YANG J Y.Building Detection form Remote Sensing Images Based on Improved U-net[J].Laser and Optoelectronics Progress,2019,657(22):195-202.
[8]LI Z,ZHOU F.FSSD:Feature Fusion Single Shot Multibox Detector[J].arXiv:1712.00960,2018.
[9]SHENG Y T,ZHAO Z,WANG T T.Building Areas Extraction in GF-3 Images Based on the Integration of Span Image and Texture Features[J].Beijing Surveying and Mapping,2020,34(1):73-78.
[10]FENG F J,LI J P,DING Y Z.Target Detection from High Re-solution Remote Sensing Images Based on Combination of Multi-scale Visual Features[J].Journal of Applied Sciences,2018,36(3):471-484.
[11]LIU S,HUANG D,WANG Y.Learning Spatial Fusion for Single-Shot Object Detection[J].arXiv:1911.09516,2019.
[12]FENG F,WANG S T.Building Extraction Based on Multi-input-multi-output and Multi-feature Fusion[J].Journal of Zhengzhou Institute of Surveying and Mapping,2020,37(6):575-580.
[13]WANG Z H,LIU H Q.Building Recognition Based on Transfer Learningand Adaptive Feature Fusion[J].Computer Technology and Development,2019(12):40-43.
[14]LI X Y.Object Detection in Remote Sensing Images Based on Deep Learning[D].Hefei:University of Science and Technology of China,2019.
[15]FENG J W,ZHANG L M,DENG X Y.Image segmentationbased on multi-source fusion FCN[J].Application Research of Computers,2018,35(9):2877-2880.
[16]ZHU G Y.Research on Building Extraction from Remote Sen-sing Images Based on Deep Lreaning[D].Hangzhou:Zhejiang University,2019.
[17]JIN F,WNAG L F,LIU Z,et al.Double U-Net Remote Sensing Image Road Extraction Method[J].Journal of Geomatics Science and Technology,2019,36(4):377-381,387.
[18]LIU R Y,SUN Q C,WANG C Y.Research on Edge Detection Algorithm in Digital Image[M].Science Press,2015.
[19]PANG Y W,XIU Y X.Lane Semantic Segmentation NeuralNetwork Based on Edge Feature Merging and Skip Connections[J].Journal of Tianjin University(Science and Technology),2019,52(8):779-787.
[20]ZHANG H,ZHAO J H,ZHANG X G.High-resolution Image Building Extraction Using U-net Neural Network[J].Remote Sensing Information,2020,35(3):143-150.
[21]XU Z H,LIU Y,QUAN J C,et al.Buildings Segmentation of Remote Sensing Images Based on U-Net Pre-encoding[J].Science Technology and Engineering,2019,19(17):250-255.
[1] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[2] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[3] NING Han-yang, MA Miao, YANG Bo, LIU Shi-chang. Research Progress and Analysis on Intelligent Cryptology [J]. Computer Science, 2022, 49(9): 288-296.
[4] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[5] WANG Run-an, ZOU Zhao-nian. Query Performance Prediction Based on Physical Operation-level Models [J]. Computer Science, 2022, 49(8): 49-55.
[6] CHEN Yong-quan, JIANG Ying. Analysis Method of APP User Behavior Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(8): 78-85.
[7] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[8] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[9] QI Xiu-xiu, WANG Jia-hao, LI Wen-xiong, ZHOU Fan. Fusion Algorithm for Matrix Completion Prediction Based on Probabilistic Meta-learning [J]. Computer Science, 2022, 49(7): 18-24.
[10] YANG Bing-xin, GUO Yan-rong, HAO Shi-jie, Hong Ri-chang. Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition [J]. Computer Science, 2022, 49(7): 57-63.
[11] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[12] DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang. Super-resolution Reconstruction of MRI Based on DNGAN [J]. Computer Science, 2022, 49(7): 113-119.
[13] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[14] LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao. Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(7): 127-131.
[15] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
Full text



No Suggested Reading articles found!