Remote Sensing Image Segmentation by Combining Feature Enhanced with Fully Convolutional Network

  • Ruiguo Yu
  • Xuzhou Fu
  • Han Jiang
  • Chenhan Wang
  • Xuewei LiEmail author
  • Mankun Zhao
  • Xiang Ying
  • Hongqian Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)


The main idea of this paper is the 25-categories classification task of remote sensing satellite image which is provided by Beijing AXIS Technology Company Limited, and proposes new methods based on fully convolutional network (FCN) and image processing. This method utilized image processing to realize color mapping and feature enhanced of remote sensing satellite image. Consider the influence of equipment and scene shooting environment, there are differences in color performance between remote sensing images, we use color mapping to improve color consistency. Aiming at the disadvantage of FCN has lower sensitivity to details, we add edge information into image as an important signal and expand the image into a five-dimensional one. Then the classification results will be attained through 25-categories classification according to FCN model. The experiment result showed the method is able to enhance the accuracy of FCN model classification to some extent.


Remote sensing image Image segmentation Feature enhanced Fully convolutional network 


  1. 1.
    Awad, M.: An unsupervised artificial neural network method for satellite image segmentation. Int. Arab J. Inf. Technol. 7(2), 199–205 (2010)MathSciNetGoogle Scholar
  2. 2.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 11(6), 679–698 (1986)CrossRefGoogle Scholar
  3. 3.
    Cheng, Y., Zhao, Y., Song, G.U.: Cloud classification of GMS-5 satellite imagery by the use of multispectral threshold technique. J. Nanjing Inst. Meteorol. 25(6), 747–754 (2002)Google Scholar
  4. 4.
    Frauman, E., Wolff, E.: Segmentation of very high spatial resolution satellite images in urban areas for segments-based classification. In: Proceedings for 3rd International Symposium Remote Sensing and Data Fusion Over Urban Areas, Tempe, Arizona (2005)Google Scholar
  5. 5.
    Fu, G., Liu, C., Zhou, R., Sun, T., Zhang, Q.: Classification for high resolution remote sensing imagery using a fully convolutional network. Rem. Sens. 9(5), 498–498 (2017)CrossRefGoogle Scholar
  6. 6.
    Jiao, L., Liang, M., Chen, H., Yang, S., Liu, H., Cao, X.: Deep fully convolutional network-based spatial distribution prediction for hyperspectral image classification. IEEE Trans. Geosci. Rem. Sens. 55(10), 5585–5599 (2017)CrossRefGoogle Scholar
  7. 7.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference On Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  8. 8.
    Mercier, G., Lennon, M.: Support vector machines for hyperspectral image classification with spectral-based kernels. In: 2003 Proceedings of IEEE International Conference on Geoscience and Remote Sensing Symposium, IGARSS 2003, vol. 1, pp. 288–290. IEEE (2003)Google Scholar
  9. 9.
    Miller, D.M., Kaminsky, E.J., Rana, S.: Neural network classification of remote-sensing data. Comput. Geosci. 21(3), 377–386 (1995)CrossRefGoogle Scholar
  10. 10.
    Mukhopadhyay, A., Maulik, U.: Unsupervised satellite image segmentation by combining SA based fuzzy clustering with support vector machine. In: 2009 Seventh International Conference on Advances in Pattern Recognition, ICAPR 2009, pp. 381–384. IEEE (2009)Google Scholar
  11. 11.
    Munandar, T., Suhendar, A., Abdullah, A., Rohendi, D., et al.: Satellite image edge detection for population distribution pattern identification using levelset with morphological filtering process. In: IOP Conference Series: Materials Science and Engineering. vol. 180, pp. 012064–012064. IOP Publishing (2017)Google Scholar
  12. 12.
    Papandreou, G., Chen, L.C., Murphy, K.P., Yuille, A.L.: Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1742–1750 (2015)Google Scholar
  13. 13.
    Roberts, L.G.: Machine perception of three-dimensional solids. Ph.D. thesis, Massachusetts Institute of Technology (1963)Google Scholar
  14. 14.
    Saha, I., Maulik, U., Bandyopadhyay, S., Plewczynski, D.: SVMeFC: SVM ensemble fuzzy clustering for satellite image segmentation. IEEE Geosci. Rem. Sens. Lett. 9(1), 52–55 (2012)CrossRefGoogle Scholar
  15. 15.
    Sharma, S., Buddhiraju, K.M., Banerjee, B.: An ant colony optimization based inter domain cluster mapping for domain adaptation in remote sensing. In: 2014 IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), pp. 2158–2161. IEEE (2014)Google Scholar
  16. 16.
    Sobel, I., Feldman, G.: A \(3\times 3\) isotropic gradient operator for image processing. A Talk at the Stanford Artificial Project, pp. 271–272 (1968)Google Scholar
  17. 17.
    Yuan, Y., Lin, J., Wang, Q.: Hyperspectral image classification via multitask joint sparse representation and stepwise MRF optimization. IEEE Trans. Cybern. 46(12), 2966–2977 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ruiguo Yu
    • 1
  • Xuzhou Fu
    • 1
  • Han Jiang
    • 2
  • Chenhan Wang
    • 2
  • Xuewei Li
    • 1
    Email author
  • Mankun Zhao
    • 1
  • Xiang Ying
    • 3
  • Hongqian Shen
    • 1
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Beijing AXIS Technology Company LimitedBeijingChina
  3. 3.School of Computer SoftwareTianjin UniversityTianjinChina

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