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模糊神经网络高分辨率遥感影像监督分类

王春艳1, 徐爱功2, 赵雪梅2, 姜勇3(1.辽宁工程技术大学矿业技术学院, 葫芦岛 125105;2.辽宁工程技术大学测绘与地理科学学院, 阜新 123000;3.辽宁工程技术大学电气与控制工程学院, 葫芦岛 125105)

摘 要
目的 针对高分辨率带来的像素类属不确定性增大及各类属间相关性增强引起的影像分类问题,提出一种模糊神经网络高分辨遥感影像监督分类方法。方法 提出的模型为包含输入层,隐含层(隶属函数层)及输出层的三层前向模糊神经网络,输入层用于接收来自训练样本的灰度值;隐含层每个神经元节点的模糊隶属函数为对各类别定义的高斯隶属函数模型,以实现对输入变量隶属程度的不确定表达;输出层的输入变量为隐含层各神经元节点输出变量的线性组合,激活函数为分段线性函数,该层实现输入变量隶属程度的相关性表达。以训练数据直方图作为期望输出,梯度下降法求解模型参数,最后按最大隶属度准则实现分类决策。结果 利用本文算法和经典算法对合成影像进行实验,本文方法总体精度达到0.931,相对于高斯隶属函数方法总体精度提高了5.3%,相对于最大似然法提高了4.2%,相对于FCM方法提高了5.9%,对真实WorldView-2全色影像的实验中文中方法分割精度也高于传统方法。结论 提出的模糊神经网络模型可以更加精确的拟合高分辨率遥感影像复杂的分布特征,有效处理高分辨率遥感影像的上述分类问题。
关键词
Supervised classification of high resolution remote sensing image based on fuzzy neural network

Wang Chunyan1, Xu Aigong2, Zhao Xuemei2, Jiang Yong3(1.School of Mining Industry and Technology, Liaoning Technical University, Huludao 125105, China;2.School of Geomatics, Liaoning Technical University, Fuxin 123000, China;3.Faxulty of Eletrical and Control Engineering Liaoning Technical University, Huludao 125105, China)

Abstract
Objective Image classification is a significant part of image processing, and the accuracy of the classification result has a considerable influence on the following processes, such as feature extraction, object recognition, and image classification.High resolution remote sensing image can present detailed information of the interesting objects, which provides a sufficient basis for precise image classification.However, new questions and difficulties exist in the classification of high resolution image.These difficulties are caused by the increasing uncertainty of the class of pixels, as well as the complexity of correlation characteristics of different classes, which are attributed to the enhancement of spectral heterogeneity in the same object and spectral similarity in different objects.For example, distribution of an object in feature space may be asymmetric, or with multi-summits and distributions expressing different objects may contain many overlapping areas.Traditional fuzzy clustering algorithms, such as fuzzy c-means (FCM) algorithm, can effectively solve the problem introduced by the uncertainty of the class of pixels and obtain satisfactory classification results for low or medium resolution remote sensing images.On the contrary, traditional fuzzy clustering algorithms cannot deal with the influence of correlations between the class of pixels on the classification results in view of the preceding characteristics of high resolution remote sensing images.Fuzzy neural network has a powerful ability on approaching the numerical solution and describing the characteristics of uncertainty.The fuzzy neural network model treats the fuzzy membership function of a pixel as a hidden input to tackle with the uncertainty of pixels and determine the interrelation by solving the model parameters of the fuzzy neural network.Thus, the fuzzy neural network can solve the problem attributed to the uncertainty of subordination of pixels and the correlation between them in high resolution remote sensing images.To this end, this paper proposes a supervised classification algorithm for high resolution remote sensing images based on improved fuzzy neural network.Method A three-layer feed-forward neural network is designed.The network contains input, hidden, and output layers.Training samples acquired by supervise sampling are used to train the network and estimate hidden parameters.Classification of the detected image is carried out by considering pixels of the detected image as inputs.In the training process, the input layer is used to accept the gray values of the training samples.For each class, the training samples are used to calculate the histogram.The input value is any existing gray level in the training samples, and the expectation output is the histogram frequency of the corresponding gray level in its class.If the gray level is not contained in the training data, then its expected frequency is zero.The function of the input layer is to transfer the data directly to the hidden layer.That is to say, the input and the hidden layers have no parameters.A fuzzy membership function in the hidden layer is defined for each node, which is the Gaussian membership function in the proposed algorithm.The fuzzy operations are performed, wherein the number of nodes is equal to the number of classes.The function is the uncertain expression of the membership degree of the input variables.The input of the output layer is the linear combination of the output of each neural node from the hidden layer.The number of neural node is equal to the number of classes, and the active function is the custom piecewise linear function.The defined active function should satisfy the following constraints.When the linear combination of the membership function of each training data falls between zero to one, the output membership function after the training process remains;When the linear combination of the membership function of each training data is less than zero, the output membership function after the training process is zero;When the linear combination of the membership function of each training data is greater than one, the output membership function after the training process is the maximum of the frequencies present in the histogram.Consider the frequencies of all the training data as the expected output and estimate the corresponding parameters through gradient descent algorithm, including the coefficients of the membership function, mean, standard deviation, as well as the weights and offsets in the summation layer.Finally, the fuzzy algorithm segments the images based on the maximum membership function.Result The proposed algorithm, Gaussian membership function algorithm, maximum likelihood algorithm, and FCM algorithm are performed on high-resolution synthetic and real remote sensing images.The fitting results of histogram from the Gaussian membership function and the proposed algorithm are displayed along with the classification results and the precision evaluation index.Qualitative and quantitative experimental results demonstrate that the proposed algorithm can characterize the asymmetry distribution exhibited in the high resolution remote sensing images and is a better fit than the Gaussian membership function algorithm.Moreover, the proposed algorithm can obtain higher accuracy results over traditional classification algorithms.Conclusion This paper proposes an improved fuzzy neural network supervision classification algorithm for high resolution remote sensing images based on the Gaussian membership function.The proposed algorithm establishes a Gaussian membership function in homogeneous regions to characterize the uncertainty of pixels and designs a fuzzy neural network model to represent the relationships between different classes.This algorithm also solves the problems attributed to high resolution remote sensing images.The fitting results on the histogram and the accuracy of the classification decision are improved.Qualitative and quantitative analysis demonstrate the ability of improving the accuracy in image classification results of the proposed algorithm through improving the fitting ability on complex distributions.Although the proposed algorithm can improve the quality of the model and the accuracy of classification results, the following problems remain.First, pixel-based image classification algorithms are sensitive to noise and outliers and cannot distinguish pixels with similar features, such as water and the shadows, without considering the spatial correlation of pixels.Therefore, spatial relationships between pixels should be considered in the following research.Second, texture features, which are important for image classification, are not involved.In future work, we will attempt to study and construct the texture features of high-resolution remote sensing images.The texture features of the detected image will be employed to develop an algorithm suitable to most kinds of images.
Keywords

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