Yang Wugu,∗ Tian Weixin,∗,∗∗ and Ming Lei∗


  1. [1] X. Huang and L. Zhang, An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery, IEEE Transactions on Geoscience and Remote Sensing, 51(1), 2013, 257–272.
  2. [2] S. Dzikitia, M.B. Gusha, D.C. Le Maitrea, A. Maherrya, N.Z. Jovanovica. A. Ramoeloab, et al., Quantifying potential water savings from clearing invasive alien Eucalyptus camaldulensis using in situ and high resolution remote sensing data in the Berg River Catchment, Western Cape, South Africa, Forest Ecology and Management, 361, 2016, 69–80.
  3. [3] H. Hongyuan, G. Jifa, and L. Zhao-Liang, Hyperspectral image classification for land cover based on an improved interval type-II fuzzy c-means approach, Sensors, 18(2), 2018, 363–376.
  4. [4] J. Tao, H. Dan, and Y. Xianchuan, Enhanced FCM algorithm using object-based triangular fuzzy set modeling for remotesensing clustering, Computers & Geosciences, 118, 2018, 14–26.
  5. [5] M. Dongping, C. Tianyu, C. Hongyue, L. Longxiang, Q. Cheng, and D. Jinyang, Semivariogram-based spatial bandwidth selection for remote sensing image segmentation with mean-shift 6 algorithm, IEEE Geoscience and Remote Sensing Letters, 9(5), 2012, 813–817.
  6. [6] J. Michel, D. Youssefi, and M. Grizonnet, Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 53(2), 2015, 952–964.
  7. [7] W. Tianjun, X. Liegang, L. Jiancheng, Z. Xiaocheng, H. Xiaodong, M. Jianghong, et al., Computationally efficient mean-shift parallel segmentation algorithm for high-resolution remote sensing images, Journal of the Indian Society of Remote Sensing, 46(11), 2018, 1805–1814.
  8. [8] S. Tengfei, Z. Shengwei, and L. Hongyu, Variable scale meanshift based method for cropland segmentation from high spatial resolution remote sensing images, Remote Sensing for Land & Resources, 29(3), 2017, 118–123.
  9. [9] R. Dong, Z. Chang, R. Shun, Z. Zhong, W. Jihua, and L. Anxiang, An improved approach of cars for Longjing tea detection based on near infrared spectra, International Journal of Robotics & Automation, 33(1), 2018, 97–103.
  10. [10] P. Shao, W. Shi, and M. Hao, Indicator-kriging-integrated evidence theory for unsupervised change detection in remotely sensed imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12), 2018, 4649– 4663.
  11. [11] M. Xuegang, D. Zihan, L. Jiaqian, C. Shuxin, and H. Jiyu, Object-oriented segmentation and classification of forest gap based on Quick Bird remote sensing image, Chinese Journal of Applied Ecology, 29(1), 2018, 44–52.
  12. [12] M. Belgiu and L. Dr˘agut¸, Random forest in remote sensing: A review of applications and future directions, ISPRS Journal of Photogrammetry & Remote Sensing, 114, 2016, 24–31.
  13. [13] Z. Hua and Z. Gaichang, Multi-scale segmentation of objectoriented GF-1 remote sensing image, Journal of Gansu Agricultural University, 53(4), 2018, 116–123.
  14. [14] T. Zhou, L. Gu, and R. Ren, Application of multi-scale segmentation algorithms for high resolution remote sensing image, Applications of Digital Image Processing XL, San Diego, California, United States, 2017.103961T
  15. [15] M. Xuegang, D. Zihan, L. Jiaqian, C. Shuxin, and H. Jiyu, Segmentation and classification of QuickBird remote sensing image based on object-oriented segmentation, Chinese Journal of Applied Ecology, 29(1), 2018, 44–52.
  16. [16] Y. Ma, D. Ming, and H. Yang, Scale estimation of objectoriented image analysis based on spectral-spatial statistics, Journal of Remote Sensing, 21(4), 2017, 566–578.
  17. [17] L. Xiaodan, Y. Ning, and Q. Hongyuan, Hierarchical multiscale vegetation segmentation of remote sensing image based on spectral histogram, Remote Sensing of Land and Resources, 29(2), 2017, 82–89.
  18. [18] W. Chunyan, L. Jiaxin, X. Aigong, W. Yu, and S. Xin, A new method of fuzzy supervised classification of high resolution remote sensing image, Geomatics & Information Science of Wuhan University, 43(6), 2018, 922–929.
  19. [19] S. Pan, S. Wenzhong, H. Pengfei, H. Ming, and Z. Xiaokang, Novel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithm, Remote Sensing, 8(3), 2016, 264.
  20. [20] J. Chunyang, L. Weihua, and L. Xiaochun, High-resolution remote sensing image segmentation based on weight adaptive fractal net evolution approach, Remote Sensing for Land & Resources, 25(4), 2013, 22–25.
  21. [21] C. Qihao, L. Linlin, X. Qiao, Y. Shuai, S. Xuguo, and L. Xiuguo, Multi-feature segmentation for high-resolution polarimetric SAR data based on fractal net evolution approach, Remote Sensing, 9(6), 2017, 570.
  22. [22] M. Lingkui, D. Ting, and Z. Wen, Drought monitoring using an Integrated Drought Condition Index (IDCI) derived from multi-sensor remote sensing data, Natural Hazards, 80(2), 2016, 1135–1152.
  23. [23] Z. Junjie, D. Xiaoping, F. Xiangtao, and G. Huadong, A advanced multi-scale fractal net evolution approach, Remote Sensing Technology and Application, 29(2), 2014, 324–329.
  24. [24] S. Tengfei, Efficient paddy field mapping using Landsat-8 imagery and object-based image analysis based on advanced fractel net evolution approach, GI Science & Remote Sensing, 54(3), 2017, 354–380.
  25. [25] M. Kai, W. Jihua, C. Zehua, Z. Xiaobo, and P. Ligang, A method for extending the geostatistical functions in spatial information processing, Intelligent Automation and Soft Computing, 22(2), 2016, 261–266.
  26. [26] S. Peirong, C. Yongfu, H. Liu, and W. Yunhua, Selection of multi-scale segmentation parameters based on segmentation evaluation function, Remote Sensing Technology and Application, 33(4), 2018, 628–637.
  27. [27] J. Luyuan, X. Pengfeng, F. Xuezhi, L. Yun, and Z. Liujun, Assessment of large-scale land cover datasets in typical areas of china based on sub-fractional error matrix, Remote Sensing Technology and Application, 30(2), 2015, 353–363.

Important Links:

Go Back