http://iet.metastore.ingenta.com
1887

access icon openaccess High-efficiency scene classification based on deep compressed-domain feature

Loading full text...

Full text loading...

/deliver/fulltext/joe/2019/19/JOE.2019.0266.html;jsessionid=hafzl01e4lu6.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fjoe.2019.0266&mimeType=html&fmt=ahah

References

    1. 1)
      • 1. Gómez-Chova, L., Tuia, D., Moser, G., et al: ‘Multimodal classification of remote sensing images: a review and future directions’, Proc. IEEE, 2015, 103, (9), pp. 15601584.
    2. 2)
      • 2. Cheng, G., Han, J., Lu, X.: ‘Remote sensing image scene classification: benchmark and state of the art’, Proc. IEEE, 2017, 105, (10), pp. 18651883.
    3. 3)
      • 3. Hu, Q., Wu, W., Xia, T., et al: ‘Exploring the use of google earth imagery and object based methods in land use/cover mapping’, Remote Sens.., 2013, 5, (11), pp. 60266042.
    4. 4)
      • 4. Gamba, P.: ‘Human settlements: a global challenge for EO data processing and interpretation’, Proc. IEEE, 2013, 101, (3), pp. 570581.
    5. 5)
      • 5. Yang, Y., Newsam, S.: ‘Bag-of-visual words and spatial extensions for land-use classification’. Proc. ACM SIGSPATIAL Int. Conf. Adv. Geogr. Inf. Syst., San Jose, CA, USA, 2010, pp. 270279.
    6. 6)
      • 6. Cheng, G., Han, J., Guo, L., et al: ‘Object detection in remote sensing imagery using a discriminatively trained mixture model’, ISPRS J. Photogramm. Remote Sens., 2013, 85, pp. 3243.
    7. 7)
      • 7. Cheng, G., Han, J., Guo, L., et al: ‘Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images’, IEEE Trans. Geosci. Remote Sens., 2015, 53, (8), pp. 42384249.
    8. 8)
      • 8. Dai, D., Yang, W.: ‘Satellite image classification via two-layer sparse coding with biased image representation’, IEEE Geosci. Remote Sens. Lett., 2011, 8, (1), pp. 173176.
    9. 9)
      • 9. Zhang, Y., Zheng, X., Liu, G., et al: ‘Semi-supervised manifold learning based multigraph fusion for highresolution remote sensing image classification’, IEEE Geosci. Remote Sens. Lett., 2014, 11, (2), pp. 464468.
    10. 10)
      • 10. Cheng, G., Zhou, P., Han, J., et al: ‘Auto-encoder-based shared mid-level visual dictionary learning for scene classification using very high resolution remote sensing images’, IET Comput. Vis., 2015, 9, (5), pp. 639647.
    11. 11)
      • 11. Zou, Q., Ni, L., Zhang, T., et al: ‘Deep learning based feature selection for remote sensing scene classification’, IEEE Geosci. Remote Sens. Lett., 2015, 12, (11), pp. 23212325.
    12. 12)
      • 12. Penatti, O.A., Nogueira, K., dos Santos, J.A.: ‘Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?’. Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. Workshops, Boston, MA, USA, 2015, pp. 4451.
    13. 13)
      • 13. Descampe, A., De Vleeschouwer, C., Vandergheynst, P.: ‘Scalable feature extraction for coarse-to-fine jpeg 2000 image classification’, IEEE Trans. Image Process., 2011, 20, (9), pp. 26362649.
    14. 14)
      • 14. JPEG 2000 image coding system: Core coding system, ITU-T Recommendation T.800, Aug. 2002.
    15. 15)
      • 15. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition’. Proc. Int. Conf. Learn. Represent., San Diego, CA, USA, 2015, pp. 113.
    16. 16)
      • 16. Szegedy, C., Liu, W., Jia, Y.: ‘Going deeper with convolutions’. Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Boston, MA, USA, December 2015, pp. 19.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2019.0266
Loading

Related content

content/journals/10.1049/joe.2019.0266
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address