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World View-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。
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 heterogene- ity 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 image
出处
《中国图象图形学报》
CSCD
北大核心
2017年第8期1135-1143,共9页
Journal of Image and Graphics
基金
辽宁省教育厅一般项目(LJYL036
LJYL012)
教育部高等学校博士学科点专项科研基金项目(20122121110007)
关键词
高分辨率遥感影像
分类
模糊神经网络
高斯隶属函数
监督学习
直方图拟合
high resolution remote sensing image
image classification
fuzzy neural network
Gaussian membership function
supervised learning
histogram fitting
