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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).
Highlights
Image segmentation is an important task in remote sensing image processing
This paper proposes a high-resolution remote sensing image supervised segmentation method based on interval type-2 fuzzy and neural network integrated with spatial relationship
Segmentation result; In Fig. 4 (c) the Fuzzy C-Means (FCM) segmentation result of Hidden Markov Random Field (HMRF) (HMRF-FCM); In Fig. 4 (d) the segmentation result by the method integrated with spatial relationship proposed in this paper
Summary
Image segmentation is an important task in remote sensing image processing. High-resolution remote sensing data can present more clearly detail information of ground objects, which greatly eliminates the uncertainty of pixel feature caused by mixed pixel and has great potential and advantages in accurate segmentation of ground objects. The algorithm uses membership function to represent the pixel uncertainty, build the object function by defining the non-similarity measure, define the deterministic function model neighborhood relationship of the neighborhood pixel spectrum measure (Cai et al, 2007; Ahmed et al, 2002) or build the correlation model (Chatzisand and Varvarigon., 2008; Liu. et al, 2007) of the neighborhood pixels by Markov Random Field (MRF), and obtain the optimum fuzzy segmentation by solving the object function These algorithms can smooth noise to a certain extent, effectively solve the problem of pixel uncertainty caused by the spatial correlation of pixels, and improve the segmentation accuracy of the algorithms, they cannot deal with the influence of uncertainty of decision-making on the segmentation results in high-resolution image segmentation. Implement the segmentation decision-making by integrating the spatial relationship
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