Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 385-391, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-385-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
21 Jun 2016
INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION
Chunyan Wang1, Aigong Xu2, Chao Li3, and Xuemei Zhao2 1School of Mining Industry and Technology, Liaoning Technical University Huludao 125105
2School of Geomatics, Liaoning Technical University, Fuxin 123000
3Yunnan Technology Center of Basic Surveying and Mapping, Yunnan 650034 China
Keywords: Interval Type-2 Fuzzy Model, High Resolution Remote Sensing Image, Footprint of Uncertainty, Image Segmentation, Neuron Networks Abstract. Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).
Conference paper (PDF, 1757 KB)


Citation: Wang, C., Xu, A., Li, C., and Zhao, X.: INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 385-391, https://doi.org/10.5194/isprs-archives-XLI-B7-385-2016, 2016.

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