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Volumn 11, Issue 3, 2018, Pages 978-989

A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening

Author keywords

Convolutional neural network (CNN); multiscale feature learning; pan sharpening; remote sensing

Indexed keywords

CONVOLUTION; DEEP LEARNING; DEEP NEURAL NETWORKS; EXTRACTION; FEATURE EXTRACTION; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGE RESOLUTION; JOB ANALYSIS; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; NEURAL NETWORKS;

EID: 85041545449     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2018.2794888     Document Type: Article
Times cited : (498)

References (55)
  • 1
    • 8344221153 scopus 로고    scopus 로고
    • A fast intensityhue-saturation fusion technique with spectral adjustment for IKONOS imagery
    • Oct.
    • T. M. Tu, P. S. Huang, C. L. Hung, and C. P. Chang, "A fast intensityhue-saturation fusion technique with spectral adjustment for IKONOS imagery, " IEEE Geosci. Remote Sens. Lett., vol. 1, no. 4, pp. 309-312, Oct. 2004.
    • (2004) IEEE Geosci. Remote Sens. Lett. , vol.1 , Issue.4 , pp. 309-312
    • Tu, T.M.1    Huang, P.S.2    Hung, C.L.3    Chang, C.P.4
  • 2
    • 0023399652 scopus 로고
    • Color enhancement of highly correlated images. 2. Channel ratio and chromaticity transformation techniques
    • Aug.
    • A. R. Gillespie, A. B. Kahle, and R. E. Walker, "Color enhancement of highly correlated images. 2. Channel ratio and chromaticity transformation techniques, " Remote Sens. Environ., vol. 22, pp. 343-365, Aug. 1987.
    • (1987) Remote Sens. Environ. , vol.22 , pp. 343-365
    • Gillespie, A.R.1    Kahle, A.B.2    Walker, R.E.3
  • 3
    • 0024471260 scopus 로고
    • Extracting spectral contrast in landsat thematic mapper image data using selective principal component analysis
    • Mar.
    • P. S. Chavez and A. Y. Kwarteng, "Extracting spectral contrast in landsat thematic mapper image data using selective principal component analysis, " Photogramm. Eng. Remote Sens., vol. 55, pp. 339-348, Mar. 1989.
    • (1989) Photogramm. Eng. Remote Sens. , vol.55 , pp. 339-348
    • Chavez, P.S.1    Kwarteng, A.Y.2
  • 5
    • 78650891876 scopus 로고    scopus 로고
    • A new adaptive component-substitutionbased satellite image fusion by using partial replacement
    • Jan.
    • J. Choi, K. Yu, and Y. Kim, "A new adaptive component-substitutionbased satellite image fusion by using partial replacement, " IEEE Trans. Geosci. Remote Sens., vol. 49, no. 1, pp. 295-309, Jan. 2011.
    • (2011) IEEE Trans. Geosci. Remote Sens. , vol.49 , Issue.1 , pp. 295-309
    • Choi, J.1    Yu, K.2    Kim, Y.3
  • 6
    • 0036821708 scopus 로고    scopus 로고
    • Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis
    • Oct.
    • B. Aiazzi, L. Alparone, S. Baronti, and A. Garzelli, "Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis, " IEEE Trans. Geosci. Remote Sens., vol. 40, no. 10, pp. 2300-2312, Oct. 2002.
    • (2002) IEEE Trans. Geosci. Remote Sens. , vol.40 , Issue.10 , pp. 2300-2312
    • Aiazzi, B.1    Alparone, L.2    Baronti, S.3    Garzelli, A.4
  • 7
    • 33846636743 scopus 로고    scopus 로고
    • Remote sensing image fusion using the curvelet transform
    • Apr.
    • F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, "Remote sensing image fusion using the curvelet transform, " Inf. Fusion, vol. 8, pp. 143-156, Apr. 2007.
    • (2007) Inf. Fusion , vol.8 , pp. 143-156
    • Nencini, F.1    Garzelli, A.2    Baronti, S.3    Alparone, L.4
  • 10
    • 0034521444 scopus 로고    scopus 로고
    • Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details
    • Dec.
    • J. G. Liu, "Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details, " Int. J. Remote Sens., vol. 21, pp. 3461-3472, Dec. 2000.
    • (2000) Int. J. Remote Sens. , vol.21 , pp. 3461-3472
    • Liu, J.G.1
  • 11
    • 85019229143 scopus 로고    scopus 로고
    • Combining component substitution and multiresolution analysis: A novel generalized BDSD pansharpening algorithm
    • Jun.
    • S. W. Zhong, Y. Zhang, Y. S. Chen, and D. Wu, "Combining component substitution and multiresolution analysis: A novel generalized BDSD pansharpening algorithm, " IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., vol. 10, no. 6, pp. 2867-2875, Jun. 2017.
    • (2017) IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. , vol.10 , Issue.6 , pp. 2867-2875
    • Zhong, S.W.1    Zhang, Y.2    Chen, Y.S.3    Wu, D.4
  • 12
    • 37449034801 scopus 로고    scopus 로고
    • Optimal MMSE pan sharpening of very high resolution multispectral images
    • Jan.
    • A. Garzelli, F. Nencini, and L. Capobianco, "Optimal MMSE pan sharpening of very high resolution multispectral images, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 1, pp. 228-236, Jan. 2008.
    • (2008) IEEE Trans. Geosci. Remote Sens. , vol.46 , Issue.1 , pp. 228-236
    • Garzelli, A.1    Nencini, F.2    Capobianco, L.3
  • 13
    • 84908052467 scopus 로고    scopus 로고
    • Pansharpening of multispectral images based on nonlocal parameter optimization
    • Apr.
    • A. Garzelli, "Pansharpening of multispectral images based on nonlocal parameter optimization, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 4, pp. 2096-2107, Apr. 2015.
    • (2015) IEEE Trans. Geosci. Remote Sens. , vol.53 , Issue.4 , pp. 2096-2107
    • Garzelli, A.1
  • 14
    • 44049095033 scopus 로고    scopus 로고
    • Bayesian data fusion for adaptable image pansharpening
    • Jun.
    • D. Fasbender, J. Radoux, and P. Bogaert, "Bayesian data fusion for adaptable image pansharpening, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 6, pp. 1847-1857, Jun. 2008.
    • (2008) IEEE Trans. Geosci. Remote Sens. , vol.46 , Issue.6 , pp. 1847-1857
    • Fasbender, D.1    Radoux, J.2    Bogaert, P.3
  • 15
    • 84869488524 scopus 로고    scopus 로고
    • Adjustable model-based fusion method for multispectral and panchromatic images
    • Dec.
    • L. Zhang, H. Shen, W. Gong, and H. Zhang, "Adjustable model-based fusion method for multispectral and panchromatic images, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 6, pp. 1693-1704, Dec. 2012.
    • (2012) IEEE Trans. Syst., Man, Cybern. B, Cybern. , vol.42 , Issue.6 , pp. 1693-1704
    • Zhang, L.1    Shen, H.2    Gong, W.3    Zhang, H.4
  • 16
    • 84888295476 scopus 로고    scopus 로고
    • A new pansharpening algorithm based on total variation
    • Jan.
    • F. Palsson, J. R. Sveinsson, and M. O. Ulfarsson, "A new pansharpening algorithm based on total variation, " IEEE Geosci. Remote Sens. Lett., vol. 11, no. 1, pp. 318-322, Jan. 2014.
    • (2014) IEEE Geosci. Remote Sens. Lett. , vol.11 , Issue.1 , pp. 318-322
    • Palsson, F.1    Sveinsson, J.R.2    Ulfarsson, M.O.3
  • 17
    • 85026995377 scopus 로고    scopus 로고
    • An integrated framework for the spatio-temporal-spectral fusion of remote sensing images
    • Dec.
    • H. Shen, X. Meng, and L. Zhang, "An integrated framework for the spatio-temporal-spectral fusion of remote sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 12, pp. 7135-7148, Dec. 2016.
    • (2016) IEEE Trans. Geosci. Remote Sens. , vol.54 , Issue.12 , pp. 7135-7148
    • Shen, H.1    Meng, X.2    Zhang, L.3
  • 18
    • 84861187814 scopus 로고    scopus 로고
    • A practical compressed sensing-based pan-sharpening method
    • Jul.
    • C. Jiang, H. Zhang, H. Shen, and L. Zhang, "A practical compressed sensing-based pan-sharpening method, " IEEE Geosci. Remote Sens. Lett., vol. 9, no. 4, pp. 629-633, Jul. 2012.
    • (2012) IEEE Geosci. Remote Sens. Lett. , vol.9 , Issue.4 , pp. 629-633
    • Jiang, C.1    Zhang, H.2    Shen, H.3    Zhang, L.4
  • 19
  • 20
    • 84883814326 scopus 로고    scopus 로고
    • Remote sensing image fusion via sparse representations over learned dictionaries
    • Sep.
    • S. Li, H. Yin, and L. Fang, "Remote sensing image fusion via sparse representations over learned dictionaries, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 9, pp. 4779-4789, Sep. 2013.
    • (2013) IEEE Trans. Geosci. Remote Sens. , vol.51 , Issue.9 , pp. 4779-4789
    • Li, S.1    Yin, H.2    Fang, L.3
  • 21
    • 84885018469 scopus 로고    scopus 로고
    • A sparse image fusion algorithm with application to pan-sharpening
    • May
    • X. X. Zhu and R. Bamler, "A sparse image fusion algorithm with application to pan-sharpening, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 5, pp. 2827-2836, May 2013.
    • (2013) IEEE Trans. Geosci. Remote Sens. , vol.51 , Issue.5 , pp. 2827-2836
    • Zhu, X.X.1    Bamler, R.2
  • 22
    • 84962128851 scopus 로고    scopus 로고
    • Image super-resolution using deep convolutional networks
    • Feb. 1
    • C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolutional networks, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 295-307, Feb. 1, 2016.
    • (2016) IEEE Trans. Pattern Anal. Mach. Intell. , vol.38 , Issue.2 , pp. 295-307
    • Dong, C.1    Loy, C.C.2    He, K.3    Tang, X.4
  • 23
    • 85021724055 scopus 로고    scopus 로고
    • Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising
    • Jul.
    • K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, "Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, " IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142-3155, Jul. 2017.
    • (2017) IEEE Trans. Image Process. , vol.26 , Issue.7 , pp. 3142-3155
    • Zhang, K.1    Zuo, W.2    Chen, Y.3    Meng, D.4    Zhang, L.5
  • 25
    • 84991609087 scopus 로고    scopus 로고
    • DehazeNet: An end-toend system for single image haze removal
    • Nov.
    • B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, "DehazeNet: An end-toend system for single image haze removal, " IEEE Trans. Image Process., vol. 25, no. 11, pp. 5187-5198, Nov. 2016.
    • (2016) IEEE Trans. Image Process. , vol.25 , Issue.11 , pp. 5187-5198
    • Cai, B.1    Xu, X.2    Jia, K.3    Qing, C.4    Tao, D.5
  • 26
    • 84950244823 scopus 로고    scopus 로고
    • Blind inpainting using the fully convolutional neural network
    • Feb.
    • N. Cai, Z. H. Su, Z. N. Lin, H. Wang, Z. J. Yang, and B. W. K. Ling, "Blind inpainting using the fully convolutional neural network, " Vis. Comput., vol. 33, pp. 249-261, Feb. 2017.
    • (2017) Vis. Comput. , vol.33 , pp. 249-261
    • Cai, N.1    Su, Z.H.2    Lin, Z.N.3    Wang, H.4    Yang, Z.J.5    Ling, B.W.K.6
  • 27
    • 84986325587 scopus 로고    scopus 로고
    • Accurate image super-resolution using very deep convolutional networks
    • Las Vegas, NV, USA
    • J. Kim, J. K. Lee, and K. M. Lee, "Accurate image super-resolution using very deep convolutional networks, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Las Vegas, NV, USA, 2016, pp. 1646-1654.
    • (2016) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 1646-1654
    • Kim, J.1    Lee, J.K.2    Lee, K.M.3
  • 28
    • 84986253618 scopus 로고    scopus 로고
    • Deeply-recursive convolutional network for image super-resolution
    • Las Vegas, NV, USA
    • J. Kim, J. K. Lee, and K. M. Lee, "Deeply-recursive convolutional network for image super-resolution, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Las Vegas, NV, USA, 2016, pp. 1637-1645.
    • (2016) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 1637-1645
    • Kim, J.1    Lee, J.K.2    Lee, K.M.3
  • 29
    • 85018922091 scopus 로고    scopus 로고
    • Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
    • X. J. Mao, C. Shen, and Y. B. Yang, "Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections, " Adv. Neural Inf. Process. Syst., pp. 2802-2810, 2016.
    • (2016) Adv. Neural Inf. Process. Syst. , pp. 2802-2810
    • Mao, X.J.1    Shen, C.2    Yang, Y.B.3
  • 30
    • 85037986953 scopus 로고    scopus 로고
    • End-to-end image super-resolution via deep and shallow convolutional networks
    • Y. Wang, L. Wang, H. Wang, and P. Li, "End-to-end image super-resolution via deep and shallow convolutional networks, " arXiv:1607. 07680, 2016.
    • (2016) ArXiv:1607.07680
    • Wang, Y.1    Wang, L.2    Wang, H.3    Li, P.4
  • 31
    • 84976384382 scopus 로고    scopus 로고
    • Deep learning for remote sensing data: A technical tutorial on the state of the art
    • Jun.
    • L. Zhang, L. Zhang, and B. Du, "Deep learning for remote sensing data: A technical tutorial on the state of the art, " IEEE Geosci. Remote Sens. Mag., vol. 4, no. 2, pp. 22-40, Jun. 2016.
    • (2016) IEEE Geosci. Remote Sens. Mag. , vol.4 , Issue.2 , pp. 22-40
    • Zhang, L.1    Zhang, L.2    Du, B.3
  • 32
    • 85027937409 scopus 로고    scopus 로고
    • A new pan-sharpening method with deep neural networks
    • May
    • W. Huang, L. Xiao, Z. Wei, H. Liu, and S. Tang, "A new pan-sharpening method with deep neural networks, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 5, pp. 1037-1041, May 2015.
    • (2015) IEEE Geosci. Remote Sens. Lett. , vol.12 , Issue.5 , pp. 1037-1041
    • Huang, W.1    Xiao, L.2    Wei, Z.3    Liu, H.4    Tang, S.5
  • 33
    • 84993982662 scopus 로고    scopus 로고
    • Pansharpening by convolutional neural networks
    • Jul.
    • G. Masi, D. Cozzolino, L. Verdoliva, and G. Scarpa, "Pansharpening by convolutional neural networks, " Remote Sens., vol. 8, Jul. 2016, Art. no. 594.
    • (2016) Remote Sens. , vol.8
    • Masi, G.1    Cozzolino, D.2    Verdoliva, L.3    Scarpa, G.4
  • 34
    • 84990041955 scopus 로고    scopus 로고
    • A review of image fusion algorithms based on the superresolution paradigm
    • Oct.
    • A. Garzelli, "A review of image fusion algorithms based on the superresolution paradigm, " Remote Sens., vol. 8, Oct. 2016, Art. no. 797.
    • (2016) Remote Sens. , vol.8
    • Garzelli, A.1
  • 39
    • 50549197532 scopus 로고
    • Some methods of speeding up the convergence of iteration methods
    • B. T. Polyak, "Some methods of speeding up the convergence of iteration methods, " USSR Comput. Math. Math. Phys., vol. 4, pp. 1-17, 1964.
    • (1964) USSR Comput. Math. Math. Phys. , vol.4 , pp. 1-17
    • Polyak, B.T.1
  • 40
    • 84913580146 scopus 로고    scopus 로고
    • Caffe: Convolutional architecture for fast feature embedding
    • Y. Jia et al., "Caffe: Convolutional architecture for fast feature embedding, " in Proc. 22nd ACM Int. Conf. Multimedia, 2014, pp. 675-678.
    • (2014) Proc. 22nd ACM Int. Conf. Multimedia , pp. 675-678
    • Jia, Y.1
  • 41
    • 84962815548 scopus 로고    scopus 로고
    • Matconvnet: Convolutional neural networks for MATLAB
    • A. Vedaldi and K. Lenc, "MatConvNet: Convolutional Neural Networks for MATLAB, " ACM Int. Conf. Multimedia, pp. 689-692, 2015.
    • (2015) ACM Int. Conf. Multimedia , pp. 689-692
    • Vedaldi, A.1    Lenc, K.2
  • 42
    • 27844607355 scopus 로고    scopus 로고
    • Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods
    • Oct.
    • X. Otazu, M. Gonzalez-Audicana, O. Fors, and J. Nunez, "Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods, " IEEE Trans. Geosci. Remote Sens., vol. 43, no. 10, pp. 2376-2385, Oct. 2005.
    • (2005) IEEE Trans. Geosci. Remote Sens. , vol.43 , Issue.10 , pp. 2376-2385
    • Otazu, X.1    Gonzalez-Audicana, M.2    Fors, O.3    Nunez, J.4
  • 44
    • 1942436689 scopus 로고    scopus 로고
    • Image quality assessment: From error visibility to structural similarity
    • Apr.
    • Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity, " IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004.
    • (2004) IEEE Trans. Image Process. , vol.13 , Issue.4 , pp. 600-612
    • Wang, Z.1    Bovik, A.C.2    Sheikh, H.R.3    Simoncelli, E.P.4
  • 45
    • 0036493909 scopus 로고    scopus 로고
    • A universal image quality index
    • Mar.
    • Z. Wang and A. C. Bovik, "A universal image quality index, " IEEE Signal Process. Lett., vol. 9, no. 3, pp. 81-84, Mar. 2002.
    • (2002) IEEE Signal Process. Lett. , vol.9 , Issue.3 , pp. 81-84
    • Wang, Z.1    Bovik, A.C.2
  • 47
    • 0001256711 scopus 로고
    • Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm
    • R. H. Yuhas, A. F. Goetz, and J. W. Boardman, "Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm, " in Proc. Annu. JPL Airborne Geosci. Workshop, 1992, pp. 147-149.
    • (1992) Proc. Annu. JPL Airborne Geosci. Workshop , pp. 147-149
    • Yuhas, R.H.1    Goetz, A.F.2    Boardman, J.W.3
  • 48
    • 70350294320 scopus 로고    scopus 로고
    • Hypercomplex quality assessment of multi-/hyper-spectral images
    • Oct.
    • A. Garzelli and F. Nencini, "Hypercomplex quality assessment of multi-/hyper-spectral images, " IEEE Geosci. Remote Sens. Lett., vol. 6, no. 4, pp. 662-665, Oct. 2009.
    • (2009) IEEE Geosci. Remote Sens. Lett. , vol.6 , Issue.4 , pp. 662-665
    • Garzelli, A.1    Nencini, F.2
  • 50
    • 84908077812 scopus 로고    scopus 로고
    • Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature
    • P. Wu, H. Shen, L. Zhang, and F. M. Göttsche, "Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature, " Remote Sens. Environ., vol. 156, pp. 169-181, 2015.
    • (2015) Remote Sens. Environ. , vol.156 , pp. 169-181
    • Wu, P.1    Shen, H.2    Zhang, L.3    Göttsche, F.M.4
  • 51
    • 84973523368 scopus 로고    scopus 로고
    • Noise removal from hyperspectral image with joint spectral-spatial distributed sparse representation
    • Sep.
    • J. Li, Q. Yuan, H. Shen, and L. Zhang, "Noise removal from hyperspectral image with joint spectral-spatial distributed sparse representation, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 9, pp. 5425-5439, Sep. 2016.
    • (2016) IEEE Trans. Geosci. Remote Sens. , vol.54 , Issue.9 , pp. 5425-5439
    • Li, J.1    Yuan, Q.2    Shen, H.3    Zhang, L.4
  • 52
    • 84867063792 scopus 로고    scopus 로고
    • Hyperspectral image denoising employing a spectral-spatial adaptive total variation model
    • Oct.
    • Q. Yuan, L. Zhang, and H. Shen, "Hyperspectral image denoising employing a spectral-spatial adaptive total variation model, " IEEE Trans. Geosci. Remote Sens., vol. 50, no. 10, pp. 3660-3677, Oct. 2012.
    • (2012) IEEE Trans. Geosci. Remote Sens. , vol.50 , Issue.10 , pp. 3660-3677
    • Yuan, Q.1    Zhang, L.2    Shen, H.3
  • 53
    • 84950141946 scopus 로고    scopus 로고
    • Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery
    • Nov.
    • F. Hu, G. S. Xia, J. W. Hu, and L. P. Zhang, "Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery, " Remote Sens., vol. 7, pp. 14680-14707, Nov. 2015.
    • (2015) Remote Sens. , vol.7 , pp. 14680-14707
    • Hu, F.1    Xia, G.S.2    Hu, J.W.3    Zhang, L.P.4
  • 54
    • 85018642692 scopus 로고    scopus 로고
    • AID: A benchmark data set for performance evaluation of aerial scene classification
    • Jul.
    • G. S. Xia et al., "AID: A benchmark data set for performance evaluation of aerial scene classification, " IEEE Trans. Geosci. Remote Sens., vol. 55, no. 7, pp. 3965-3981, Jul. 2017.
    • (2017) IEEE Trans. Geosci. Remote Sens. , vol.55 , Issue.7 , pp. 3965-3981
    • Xia, G.S.1
  • 55
    • 84973571837 scopus 로고    scopus 로고
    • Weakly supervised learning based on coupled convolutional neural networks for aircraft detection
    • Sep.
    • F. Zhang, B. Du, L. Zhang, and M. Xu, "Weakly supervised learning based on coupled convolutional neural networks for aircraft detection, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 9, pp. 5553-5563, Sep. 2016.
    • (2016) IEEE Trans. Geosci. Remote Sens. , vol.54 , Issue.9 , pp. 5553-5563
    • Zhang, F.1    Du, B.2    Zhang, L.3    Xu, M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.