激光与光电子学进展, 2021, 58 (4): 0415006, 网络出版: 2021-02-22   

基于卷积特征和贝叶斯决策的双波段场景分类 下载: 714次

Dual-Band Scene Classification Based on Convolutional Features and Bayesian Decision
作者单位
1 火箭军工程大学作战保障学院, 陕西 西安 710025
2 中国人民武装警察部队工程大学信息工程学院, 陕西 西安 710086
引用该论文

邱晓华, 李敏, 张丽琼, 董琳. 基于卷积特征和贝叶斯决策的双波段场景分类[J]. 激光与光电子学进展, 2021, 58(4): 0415006.

Xiaohua Qiu, Min Li, Liqiong Zhang, Lin Dong. Dual-Band Scene Classification Based on Convolutional Features and Bayesian Decision[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0415006.

参考文献

[1] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

[2] SimonyanK, ZissermanA. Very deep convolutional networks for large-scale image recognition[EB/OL]. [2020-06-15].http:∥arxiv.org/abs/1409. 1556.

[3] SzegedyC, VanhouckeV, IoffeS, et al.Rethinking the inception architecture for computer vision[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA.New York: IEEE Press, 2016: 2818- 2826.

[4] He KM, Zhang XY, Ren SQ, et al.Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA.New York: IEEE Press, 2016: 770- 778.

[5] Jiang J H, Feng X A, Liu F, et al. Multi-spectral RGB-NIR image classification using double-channel CNN[J]. IEEE Access, 2019, 7: 20607-20613.

[6] 刘峰, 沈同圣, 马新星. 特征融合的卷积神经网络多波段舰船目标识别[J]. 光学学报, 2017, 37(10): 1015002.

    Liu F, Shen T S, Ma X X. Convolutional neural network based multi-band ship target recognition with feature fusion[J]. Acta Optica Sinica, 2017, 37(10): 1015002.

[7] Ding L, Wang Y, Laganière R, et al. Convolutional neural networks for multispectral pedestrian detection[J]. Signal Processing: Image Communication, 2020, 82: 115764.

[8] Zhang Q, Huang N C, Yao L, et al. RGB-T salient object detection via fusing multi-level CNN features[J]. IEEE Transactions on Image Processing, 2020, 29: 3321-3335.

[9] Zhang X C, Ye P, Peng S Y, et al. DSiamMFT: an RGB-T fusion tracking method via dynamic Siamese networks using multi-layer feature fusion[J]. Signal Processing: Image Communication, 2020, 84: 115756.

[10] Xie L, Lee F, Liu L, et al. Scene recognition: a comprehensive survey[J]. Pattern Recognition, 2020, 102: 107205.

[11] BrownM, SüsstrunkS. Multi-spectral SIFT for scene category recognition[C]∥CVPR 2011, June 20-25, 2011, Providence, RI, USA.New York: IEEE Press, 2011: 177- 184.

[12] SalamatiN, LarlusD, CsurkaG. Combining visible and near-infrared cues for image categorisation[C]∥22nd British Machine Vision Conference (BMVC 2011), August 30-September 1, 2011, Dundee, Scotland.UK: BMVA Press, 2011: 1- 11.

[13] Xiao Y, Wu J X. Yuan J S. mCENTRIST: a multi-channel feature generation mechanism for scene categorization[J]. IEEE Transactions on Image Processing, 2014, 23(2): 823-836.

[14] 张秋实, 李伟, 李禄, 等. 基于无字典模型的红外与可见光图像融合分类[J]. 北京化工大学学报(自然科学版), 2018, 45(2): 71-76.

    Zhang Q S, Li W, Li L, et al. Infrared and visible image fusion classification based on a codebookless model(CLM)[J]. Journal of Beijing University of Chemical Technology (Natural Science Edition), 2018, 45(2): 71-76.

[15] ŠevoI, AvramovićA. Multispectral scene recognition based on dual convolutional neural networks[C]∥Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, September 18-20, 2017, Ljubljana, Slovenia.New York: IEEE Press, 2017: 126- 130.

[16] Peng X S, Li Y X, Wei X, et al. RGB-NIR image categorization with prior knowledge transfer[J]. EURASIP Journal on Image and Video Processing, 2018, 2018(1): 1-11.

[17] 江泽涛, 秦嘉奇, 胡硕. 基于多路卷积神经网络的多光谱场景识别方法[J]. 计算机科学, 2019, 46(9): 265-270.

    Jiang Z T, Qin J Q, Hu S. Multi-spectral scene recognition method based on multi-way convolution neural network[J]. Computer Science, 2019, 46(9): 265-270.

[18] YosinskiJ, CluneJ, BengioY, et al. How transferable are features in deep neural networks? [EB/OL]. [2020-06-13].https:∥arxiv. org/abs/1411. 1792v1.

[19] Zhao H H, Liu H. Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition[J]. Granular Computing, 2020, 5(3): 411-418.

[20] Woźniak M, Graña M, Corchado E. A survey of multiple classifier systems as hybrid systems[J]. Information Fusion, 2014, 16: 3-17.

[21] Zeng H, Yang B, Wang X Q, et al. RGB-D object recognition using multi-modal deep neural network and DS evidence theory[J]. Sensors, 2019, 19(3): 529.

[22] 唐聪, 凌永顺, 杨华, 等. 基于深度学习的红外与可见光决策级融合检测[J]. 红外与激光工程, 2019, 48(6): 456-470.

    Tang C, Ling Y S, Yang H, et al. Decision-level fusion detection for infrared and visible spectra based on deep learning[J]. Infrared and Laser Engineering, 2019, 48(6): 456-470.

[23] Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828.

[24] Zeiler MD, FergusR. Visualizing and understanding convolutional networks[EB/OL]. [2020-06-15].https:∥arxiv.org/abs/1311. 2901.

[25] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 229- 232.

    Zhou ZH. Machine learning[M]. Beijing: Tsinghua University Press, 2016: 229- 232.

[26] Lin H T, Lin C J, Weng R C. A note on Platt's probabilistic outputs for support vector machines[J]. Machine Learning, 2007, 68(3): 267-276.

邱晓华, 李敏, 张丽琼, 董琳. 基于卷积特征和贝叶斯决策的双波段场景分类[J]. 激光与光电子学进展, 2021, 58(4): 0415006. Xiaohua Qiu, Min Li, Liqiong Zhang, Lin Dong. Dual-Band Scene Classification Based on Convolutional Features and Bayesian Decision[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0415006.

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