Vehicle detection method based on remote sensing image fusion of superpixel and multi-modal sensing network

被引：1
|
作者：
Lian Y. [1 ,2 ]
Li G. [1 ]
Shen S. [1 ]
机构：
[1] China University of Petroleum, Beijing
[2] Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum., Beijing
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2023年 / 31卷 / 06期
关键词：
multimodal perception; remote-sensing image; super pixel; vehicle detector;
D O I：
10.37188/OPE.20233106.0905
中图分类号：
学科分类号：
摘要：
A remote sensing image vehicle detection method combining superpixels and a multi-modal perception network is proposed with the purpose of reducing recognition accuracy due to background interference，target density，and target heterogeneity in remote sensing image vehicle detection. First，based on the region merging rules of hybrid superpixels，the superpixel bipartite graph fusion algorithm was used to fuse the superpixel segmentation results of the two modalities，which improved the accuracy of the superpixel segmentation results of different modal images. Second，MEANet，a vehicle detection method of remote sensing images based on a multi-modal edge aware network，was proposed. An optimized feature pyramid network module was introduced to enhance the ability of the network to learn multi-scale target features. Finally，the two sets of edge features generated by the superpixel and multi-modal fusion module were aggregated through the edge perception module，and the accurate boundary of the vehicle target was generated. Experiments were conducted on the ISPRS Potsdam and ISPRS Vaihingen remote sensing image datasets，and the final scores were 91. 05% and 85. 11%，respectively. The experimental results showed that the method proposed in this study has good detection accuracy and good application value in high-precision vehicle detection of multi-modal remote sensing images. © 2023 Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：905 / 919
页数：14
相关论文
共 34 条
  • [21] Indoor Semantic Segmentation using depth information［EB/OL］, (2013)
  • [22] FAN D P，, LIN Z，, ZHANG Z，, Et al., Rethinking RGB-D salient object detection： models， data sets，and large-scale benchmarks［J］, IEEE Transactions on Neural Networks and Learning Systems, 32, 5, pp. 2075-2089, (2021)
  • [23] DU SH J，, ZOU ZH R，, ZHANG Y SH, Et al., A building extraction method via graph cuts algorithm by fusion of LiDAR point cloud and orthoimage ［J］, Acta Geodaetica et Cartographica Sinica, 47, 4, pp. 519-527, (2018)
  • [24] SHEN J B, LIANG Z Y，, Et al., Real-time superpixel segmentation by DBSCAN clustering algorithm［J］, IEEE Transactions on Image Processing：a Publication of the IEEE Signal Processing Society, 25, 12, pp. 5933-5942, (2016)
  • [25] SHEN J B, WANG W G，, Et al., Lazy random walks for superpixel segmentation［J］, IEEE Transactions on Image Processing：a Publication of the IEEE Signal Processing Society, 23, 4, pp. 1451-1462, (2014)
  • [26] CHEN J S, HUANG B., Linear spectral clustering superpixel［J］, IEEE Transactions on Image Processing：a Publication of the IEEE Signal Processing Society, 26, 7, pp. 3317-3330, (2017)
  • [27] VAN DEN BERGH M, Et al., SEEDS：superpixels extracted via energy-driven sampling［J］, Computer Vision, pp. 13-26, (2012)
  • [28] CAO X C., Topology Preserved Regular Superpixel［C］, 2012 IEEE International Conference on Multimedia and Expo, pp. 765-768, (2012)
  • [29] DARRELL T., Fully Convolutional Networks for Semantic Segmentation［C］, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 640-651, (2016)
  • [30] BROX T., U-net：convolutional networks for biomedical image segmentation［J］, Lecture Notes in Computer Science, pp. 234-241, (2015)
1234