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


  1. [1] X. Huang and L. Zhang, An SVM ensemble approach combiningspectral, structural, and semantic features for the classificationof high-resolution remotely sensed imagery, IEEE Transactionson 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 watersavings from clearing invasive alien Eucalyptus camaldulensisusing in situ and high resolution remote sensing data in theBerg River Catchment, Western Cape, South Africa, ForestEcology and Management, 361, 2016, 69–80.
  3. [3] H. Hongyuan, G. Jifa, and L. Zhao-Liang, Hyperspectral imageclassification for land cover based on an improved intervaltype-II fuzzy c-means approach, Sensors, 18(2), 2018, 363–376.
  4. [4] J. Tao, H. Dan, and Y. Xianchuan, Enhanced FCM algorithmusing object-based triangular fuzzy set modeling for remote-sensing 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 se-lection for remote sensing image segmentation with mean-shift6algorithm, IEEE Geoscience and Remote Sensing Letters, 9(5),2012, 813–817.
  6. [6] J. Michel, D. Youssefi, and M. Grizonnet, Stable mean-shiftalgorithm and its application to the segmentation of arbitrarilylarge remote sensing images, IEEE Transactions on Geoscienceand Remote Sensing, 53(2), 2015, 952–964.
  7. [7] W. Tianjun, X. Liegang, L. Jiancheng, Z. Xiaocheng, H.Xiaodong, M. Jianghong, et al., Computationally efficientmean-shift parallel segmentation algorithm for high-resolutionremote sensing images, Journal of the Indian Society of RemoteSensing, 46(11), 2018, 1805–1814.
  8. [8] S. Tengfei, Z. Shengwei, and L. Hongyu, Variable scale mean-shift based method for cropland segmentation from high spatialresolution 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 teadetection based on near infrared spectra, International Journalof Robotics & Automation, 33(1), 2018, 97–103.
  10. [10] P. Shao, W. Shi, and M. Hao, Indicator-kriging-integratedevidence theory for unsupervised change detection in remotelysensed imagery, IEEE Journal of Selected Topics in AppliedEarth 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 gapbased on Quick Bird remote sensing image, Chinese Journalof 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 Journalof Photogrammetry & Remote Sensing, 114, 2016, 24–31.
  13. [13] Z. Hua and Z. Gaichang, Multi-scale segmentation of object-oriented GF-1 remote sensing image, Journal of Gansu Agri-cultural University, 53(4), 2018, 116–123.
  14. [14] T. Zhou, L. Gu, and R. Ren, Application of multi-scalesegmentation algorithms for high resolution remote sensingimage, 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 sensingimage based on object-oriented segmentation, Chinese Journalof Applied Ecology, 29(1), 2018, 44–52.
  16. [16] Y. Ma, D. Ming, and H. Yang, Scale estimation of object-oriented 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 multi-scale vegetation segmentation of remote sensing image basedon 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 newmethod of fuzzy supervised classification of high resolutionremote sensing image, Geomatics & Information Science ofWuhan 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 ona robust semi-supervised FCM clustering algorithm, RemoteSensing, 8(3), 2016, 264.
  20. [20] J. Chunyang, L. Weihua, and L. Xiaochun, High-resolutionremote sensing image segmentation based on weight adaptivefractal 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 polarimetricSAR data based on fractal net evolution approach, RemoteSensing, 9(6), 2017, 570.
  22. [22] M. Lingkui, D. Ting, and Z. Wen, Drought monitoring usingan Integrated Drought Condition Index (IDCI) derived frommulti-sensor remote sensing data, Natural Hazards, 80(2),2016, 1135–1152.
  23. [23] Z. Junjie, D. Xiaoping, F. Xiangtao, and G. Huadong, Aadvanced multi-scale fractal net evolution approach, RemoteSensing Technology and Application, 29(2), 2014, 324–329.
  24. [24] S. Tengfei, Efficient paddy field mapping using Landsat-8imagery and object-based image analysis based on advancedfractel 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 spa-tial information processing, Intelligent Automation and SoftComputing, 22(2), 2016, 261–266.
  26. [26] S. Peirong, C. Yongfu, H. Liu, and W. Yunhua, Selectionof multi-scale segmentation parameters based on segmenta-tion evaluation function, Remote Sensing Technology andApplication, 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 areasof china based on sub-fractional error matrix, Remote SensingTechnology and Application, 30(2), 2015, 353–363.

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