计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 140-145.doi: 10.11896/jsjkx.200800002
赫晓慧1, 邱芳冰2, 程淅杰2, 田智慧1, 周广胜3
HE Xiao-hui1, QIU Fang-bing2, CHENG Xi-jie2, TIAN Zhi-hui1, ZHOU Guang-sheng3
摘要: 高分辨率遥感图像建筑物目标检测在国土规划、地理监测、智慧城市等领域有着广泛的应用价值,但是由于遥感图像背景复杂,建筑物目标的部分细节特征与背景区分度较低,在进行检测任务时,容易出现建筑物轮廓失真、缺失等问题。针对这一问题,设计了自适应加权边缘特征融合网络(VAF-Net)。该方法针对遥感图像建筑物检测任务,对经典编解码器网络U-Net进行拓展,通过融合RGB特征图和边缘特征图,弥补了基础网络学习中的细节特征缺失;同时,借助网络的学习自动更新融合权重,实现自适应加权融合,充分利用不同特征的互补信息。该方法在Massachusetts Buildings数据集上进行了实验,其准确率、召回率和F1-score分别达到了82.1%,82.5%和82.3%,综合指标F1-score相比于基础网络提升了约6%。VAF-Net有效提高了编解码器网络对于高分影像建筑物目标检测任务的表现性能,具有良好的实用价值。
中图分类号:
[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. |
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