Dr. Yanfei Zhong Profile

Dr. Yanfei Zhong

at Wuhan Univ

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Publications (3)

SPIE Journal Paper | 25 April 2016

Large patch convolutional neural networks for the scene classification of high spatial resolution imagery

Yanfei Zhong, Feng Fei, Liangpei Zhang

JARS, Vol. 10, Issue 02, 025006, (April 2016) https://doi.org/10.1117/12.10.1117/1.JRS.10.025006

KEYWORDS: Convolutional neural networks, Performance modeling, Data modeling, Scene classification, Remote sensing, Spatial resolution, Earth observing sensors, High resolution satellite images, Statistical modeling, Stochastic processes

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SPIE Journal Paper | 27 July 2015

Scene classification based on multifeature probabilistic latent semantic analysis for high spatial resolution remote sensing images

Yanfei Zhong, Ming Cui, Qiqi Zhu, Liangpei Zhang

JARS, Vol. 9, Issue 01, 095064, (July 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.095064

KEYWORDS: Scene classification, Visualization, Remote sensing, Spatial resolution, Image processing, Expectation maximization algorithms, Classification systems, Image classification, Visual process modeling, Mining

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Proceedings Article | 29 December 2008 Paper

A radial basis function neural network based on artificial immune systems for remote sensing image classification

Qin Yan, Yanfei Zhong

Proceedings Volume 7285, 72850I (2008) https://doi.org/10.1117/12.815962

KEYWORDS: Remote sensing, Image classification, Neural networks, Data modeling, Data centers, Detection and tracking algorithms, Molecules, Vegetation, Artificial intelligence, Roads

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The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre-defined number of iterations. The classification results are evaluated by comparing with that of the k-means center selection procedures and other results from the literature using remote sensing imagery. It is shown that aiNet-RBF NN algorithm outperform other algorithms and provides an effective option for remote sensing image classification.

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