High Resolution Remote Sensing Image

被引：0
|
作者：
Wang C. [1 ]
Liu J. [2 ]
Xu A. [3 ]
Wang Y. [3 ]
Sui X. [3 ]
机构：
[1] School of Mining Industry and Technology, Liaoning Technical University, Huludao
[2] Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao
[3] School of Geomatics, Liaoning Technical University, Fuxin
来源：
Xu, Aigong (xu_ag@126.com) | 2018年 / Editorial Board of Medical Journal of Wuhan University卷 / 43期
基金：
中国国家自然科学基金;
关键词：
Fuzzy membership function; Fuzzy neural networks; High resolution remote sensing image; Image classification;
D O I：
10.13203/j.whugis20150726
中图分类号：
学科分类号：
摘要：
A New Method of Fuzzy Supervised Classification of This paper presents a supervised image classification method based on fuzzy membership function to solve incorrect classification of high resolution remote sensing image, which caused by highlight detail information, the uncertainly of the pixels classification derived from the increase of the differences between pixels in the homogenous region, the uncertainly of classification decision and so on. First, Gaussian model is used to characterize the uncertainly of the membership of pixels; then the model is extended to build the image fuzzy membership function to define the uncertainly of the homogenous regions. To segment the image, the objective function is built by linear function of neural network, which the fuzzy membership functions and the membership degrees of the original fuzzy membership functions as input values. The proposed method is compared with the classification methods tested on the WorldView-2 panchromatic synthetic and real images. Through the qualitative and quantitative experiments, it can be found that the proposed method has better classification accuracy. © 2018, Research and Development Office of Wuhan University. All right reserved.
引用
收藏
页码：922 / 929
页数：7
相关论文
共 20 条
  • [1] Bruzzone L., Carlin L., A Multilevel Context-based System for Classification of Very High Spatial Re-solution Images, IEEE Transactions on Geoscience and Remote Sensing, 44, 9, pp. 2587-2600, (2006)
  • [2] Gath I., Geva A.B., Unsupervised Optimal Fuzzy Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 7, pp. 773-780, (1989)
  • [3] Ahmed M.N., Yamany S.M., Mohamed N.A., Et al., A Modified Fuzzy C-means Algorithm for Bias Field Estimation and Segmentation of MRI Data, IEEE Transactions on Medical Imaging, 21, 3, pp. 193-199, (2002)
  • [4] Chen S., Zhang D., Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-induced Distance Measure, IEEE Tran-sactions on Cybernetics Systems, Man and Cyberne-tics, 34, 4, pp. 1907-1916, (2004)
  • [5] Szilagyi L., Benyo Z., Szilagyi S.M., Et al., MR Brain Image Segmentation Using an Enhanced Fuzzy C-means Algorithm, Proceedings of the 25th Annual International Conference of the IEEE Enginee-ring in Medicine and Biology Society, (2003)
  • [6] Wang H., Deng Y., Wang L., Et al., A C - means Algorithm Based on Data Field, Geoma-tics and Information Science of Wuhan University, 34, 5, pp. 626-629, (2009)
  • [7] Chatzisand S.P., Varvarigon T.A., A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation, IEEE Transactions on Fuzzy System, 16, 5, pp. 1351-1361, (2008)
  • [8] Sarkar A., Banerjee A., Banerjee A., Et al., Land Co-ver Classification in MRF Context Using Dempster-Shafer Fusion for Multisensory Imagery, IEEE Transactions on Image Processing, 14, 5, pp. 634-645, (2005)
  • [9] Karmakar S., Mujumdar P.P., Grey Fuzzy Optimization Model for Water Quality Management of a Rriver System, Advances in Water Resources, 29, 7, pp. 1088-1105, (2006)
  • [10] Oberthur T., Dobermann A., Aylward M., Using Auxiliary Information to Adjust Fuzzy Membership Functions for Improved Mapping of Soil Qualities, Geographical Information Science, 14, 5, pp. 431-454, (2000)
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