Interval Type-2 Fuzzy Based Neural Network for High Resolution Remote Sensing Image Segmentation

Abstract

ABSTRACT：Recently, high resolution remote sensing image segmentation is a hot issue in image processing 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, the 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 the neuron network which acts as the decision model in the 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).

Review of Recent Type-2 Fuzzy Image Processing Applications

“…In Wang Chunyan et al (2016) [30], a supervised image segmentation algorithm was proposed, where T1 and T2 fuzzy models are used in order to improve the performance of the final model. Qualitative and quantitative analysis demonstrated that it had better accuracy than other common techniques when using both synthetic image datasets and panchromatic images.…”