模糊神经网络高分辨率遥感影像监督分类
|
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 heterogeneity in the same object and spectral similarity in different objects. For example,distribution of an object in feature spacemay 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 threelayer 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 outliersand 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.
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 spacemay 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 threelayer 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 outliersand 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.
引文
[1]Bruzzone L,Carlin L.A multilevel context-based system for classification of very high spatial resolution images[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(9):2587-2600.[DOI:10.1109/TGRS.2006.875360]
[2]Gath I,Geva A B.Unsupervised optimal fuzzy clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):773-780.[DOI:10.1109/34.192473]
[3]Ahmed M N,Yamany S M,Mohamed N,et al.A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data[J].IEEE Transactions on Medical Imaging,2002,21(3):193-199.[DOI:10.1109/42.996338]
[4]Chen S C,Zhang D Q.Robust image segmentation using FCMwith spatial constraints based on new kernel-induced distance measure[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B(Cybernetics),2004,34(4):1907-1916.[DOI:10.1109/TSMCB.2004.831165]
[5]Szilagyi L,Benyo Z,Szilagyi S M,et al.MR brain image segmentation using an enhanced fuzzy c-means algorithm[C]//Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Cancún,Mexico:IEEE,2003,1:724-726.[DOI:10.1109/IEMBS.2003.1279866]
[6]Wang H J,Deng Y,Wang L,et al.A c-means algorithm based on data field[J].Geomatics and Information Science of Wuhan University,2009,34(5):626-629.[王海军,邓羽,王丽,等.基于数据场的C均值聚类方法研究[J].武汉大学学报信息科学版,2009,34(5):626-629]
[7]Chatzis S P,Varvarigo T A.A fuzzy clustering approach toward hidden Markov random field models for enhanced spatially constrained image segmentation[J].IEEE Transactions on Fuzzy System,2008,16(5):1351-1361.[DOI:10.1109/TFUZZ.2008.2005008]
[8]Lee S C,Lee E T.Fuzzy sets and neural networks[J].Journal of Cybernetics,1974,4(2):83-103.
[9]Lee S C,Lee E T.Fuzzy neural networks[J].Mathematical Biosciences,1975,23(1-2):151-177.[DOI:10.1016/0025-5564(75)90125-X]
[10]Chai S H,Lim J S.Economic turning point forecasting using the fuzzy neural network and non-overlap area distribution measurement method[J].The Korean Economic Review,2007,23(1):111-130.
[11]Van Gestel T,Suykens J A K,Baestaens D E,et al.Financial time series prediction using least squares support vector machines within the evidence framework[J].IEEE Transactions on Neural Networks,2001,12(4):809-821.[DOI:10.1109/72.935093]
[12]Wai R J,Chen M W,Liu Y K.Design of adaptive control and fuzzy neural network control for single-stage boost inverter[J].IEEE Transactions on Industrial Electronics,2015,62(9):5434-5445.[DOI:10.1109/TIE.2015.2408571]
[13]Kim K J.Artificial neural networks with evolutionary instance selection for financial forecasting[J].Expert Systems with Applications,2006,30(3):519-526.[DOI:10.1016/j.eswa.2005.10.007]
[14]Lim J S.Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system[J].IEEETransactions on Neural Networks,2009,20(3):522-527.[DOI:10.1109/TNN.2008.2012031]
[15]Lim J S,Ryu T W,Kim H J,et al.Feature selection for specific antibody deficiency syndrome by neural network with weighted fuzzy membership functions[C]//Proceedings of International Conference on Fuzzy Systems and Knowledge Discovery.Berlin Heidelberg:Springer,2005,3614:811-820.[DOI:10.1007/11540007_101]
[16]Qu J S,Qu S B,Wang Z J.Feature-based fuzzy-neural network approach for target classification and recognition in remote sensing images[J].Journal of Remote Sensing,2009,13(1):67-74.[瞿继双,瞿松柏,王自杰.基于特征的模糊神经网络遥感图像目标分类识别[J].遥感学报,2009,13(1):67-74].[DOI:10.11834/jrs.20090109]
[17]Tang W M.Fuzzy SVM with a new fuzzy membership function to solve the two-class problems[J].Neural Processing Letters,2011,34(3):209-219.[DOI:10.1007/s11063-011-9192-y]
[18]Filatova E S,Filatov D M,Stotckaia A D.Genetic algorithm to the formation of the optimal structure of fuzzy neural network[C]//Proceedings of the 2015 XVIII International Conference on Soft Computing and Measurements(SCM).St.Petersburg:IEEE,2015:147-149.[DOI:10.1109/SCM.2015.7190438]
[19]Sanchez D,Melin P.Modular neural network with fuzzy integration and its optimization using genetic algorithms for human recognition based on iris,ear and voice biometrics[M]//Melin P,Kacprzyk J,Pedrycz W.Soft Computing for Recognition Based on Biometrics.Berlin Heidelberg:Springer,2010,312:85-102.[DOI:10.1007/978-3-642-15111-8_6]
[2]Gath I,Geva A B.Unsupervised optimal fuzzy clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):773-780.[DOI:10.1109/34.192473]
[3]Ahmed M N,Yamany S M,Mohamed N,et al.A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data[J].IEEE Transactions on Medical Imaging,2002,21(3):193-199.[DOI:10.1109/42.996338]
[4]Chen S C,Zhang D Q.Robust image segmentation using FCMwith spatial constraints based on new kernel-induced distance measure[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B(Cybernetics),2004,34(4):1907-1916.[DOI:10.1109/TSMCB.2004.831165]
[5]Szilagyi L,Benyo Z,Szilagyi S M,et al.MR brain image segmentation using an enhanced fuzzy c-means algorithm[C]//Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Cancún,Mexico:IEEE,2003,1:724-726.[DOI:10.1109/IEMBS.2003.1279866]
[6]Wang H J,Deng Y,Wang L,et al.A c-means algorithm based on data field[J].Geomatics and Information Science of Wuhan University,2009,34(5):626-629.[王海军,邓羽,王丽,等.基于数据场的C均值聚类方法研究[J].武汉大学学报信息科学版,2009,34(5):626-629]
[7]Chatzis S P,Varvarigo T A.A fuzzy clustering approach toward hidden Markov random field models for enhanced spatially constrained image segmentation[J].IEEE Transactions on Fuzzy System,2008,16(5):1351-1361.[DOI:10.1109/TFUZZ.2008.2005008]
[8]Lee S C,Lee E T.Fuzzy sets and neural networks[J].Journal of Cybernetics,1974,4(2):83-103.
[9]Lee S C,Lee E T.Fuzzy neural networks[J].Mathematical Biosciences,1975,23(1-2):151-177.[DOI:10.1016/0025-5564(75)90125-X]
[10]Chai S H,Lim J S.Economic turning point forecasting using the fuzzy neural network and non-overlap area distribution measurement method[J].The Korean Economic Review,2007,23(1):111-130.
[11]Van Gestel T,Suykens J A K,Baestaens D E,et al.Financial time series prediction using least squares support vector machines within the evidence framework[J].IEEE Transactions on Neural Networks,2001,12(4):809-821.[DOI:10.1109/72.935093]
[12]Wai R J,Chen M W,Liu Y K.Design of adaptive control and fuzzy neural network control for single-stage boost inverter[J].IEEE Transactions on Industrial Electronics,2015,62(9):5434-5445.[DOI:10.1109/TIE.2015.2408571]
[13]Kim K J.Artificial neural networks with evolutionary instance selection for financial forecasting[J].Expert Systems with Applications,2006,30(3):519-526.[DOI:10.1016/j.eswa.2005.10.007]
[14]Lim J S.Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system[J].IEEETransactions on Neural Networks,2009,20(3):522-527.[DOI:10.1109/TNN.2008.2012031]
[15]Lim J S,Ryu T W,Kim H J,et al.Feature selection for specific antibody deficiency syndrome by neural network with weighted fuzzy membership functions[C]//Proceedings of International Conference on Fuzzy Systems and Knowledge Discovery.Berlin Heidelberg:Springer,2005,3614:811-820.[DOI:10.1007/11540007_101]
[16]Qu J S,Qu S B,Wang Z J.Feature-based fuzzy-neural network approach for target classification and recognition in remote sensing images[J].Journal of Remote Sensing,2009,13(1):67-74.[瞿继双,瞿松柏,王自杰.基于特征的模糊神经网络遥感图像目标分类识别[J].遥感学报,2009,13(1):67-74].[DOI:10.11834/jrs.20090109]
[17]Tang W M.Fuzzy SVM with a new fuzzy membership function to solve the two-class problems[J].Neural Processing Letters,2011,34(3):209-219.[DOI:10.1007/s11063-011-9192-y]
[18]Filatova E S,Filatov D M,Stotckaia A D.Genetic algorithm to the formation of the optimal structure of fuzzy neural network[C]//Proceedings of the 2015 XVIII International Conference on Soft Computing and Measurements(SCM).St.Petersburg:IEEE,2015:147-149.[DOI:10.1109/SCM.2015.7190438]
[19]Sanchez D,Melin P.Modular neural network with fuzzy integration and its optimization using genetic algorithms for human recognition based on iris,ear and voice biometrics[M]//Melin P,Kacprzyk J,Pedrycz W.Soft Computing for Recognition Based on Biometrics.Berlin Heidelberg:Springer,2010,312:85-102.[DOI:10.1007/978-3-642-15111-8_6]