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. 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. 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. 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.