【作者】 陈芳杰；

【导师】 汤文兵；

【作者基本信息】 安徽理工大学 ， 计算机应用技术， 2012， 硕士

【摘要】 遥感技术是一种能够对目标进行非接触测量,采集并且分析的一种新型探测技术。随着电子计算机和空间技术的发展,各种资源和环境卫星的发射和成功的运行,通过遥感卫星从太空的高度对地球全貌以及地表动态变化等各种资源信息的提取技术得到了快速的发展。因此,对获得的海量遥感图像进行识别处理,即通过提取图像信息的特征,并利用这些特征进行图像分类,进而达到图像识别一直是遥感技术所要解决的重要问题之一。神经网络因特有的自组织学习和强大的容错性,并且在解决非线性映射问题上表现出独特的功能,使得神经网络比传统的统计参数分类方法上表现出很大的优势,还能有一定的抗噪能力,被广泛的应用在模式识别以及图像处理等各种领域。针对已经有用于遥感图像处理的神经网络系统,本文对其应用最广泛的BP神经网络进行了介绍。由于标准的BP算法在对图像分类时存在着收敛速度慢以及对网络初始参数的依赖,容易陷入局部最小值问题。针对BP算法的缺点,我们利用具有全局搜索能力、强继承性的遗传算法对BP初始网络参数进行训练,得到的最优染色体可以提高分类效率和解决局部极值的问题。因此本文构造了基于GA-BP的三层神经网络用于遥感图像进行分类的方法。一般在处理遥感图像分类问题时采用的是基于光谱特征的分类方法,即针对遥感图像的灰度特征进行分类,采用单特征进行分类的精度不高,所以本文采用的是基于光谱和纹理特征相融合的分类方法。对于光谱特征的提取,采用的是对整幅图像进行加窗提取窗口内像素的平均灰度特征的方法,并用提取的平均灰度特征来表示该窗口区域的特征值。对于纹理特征提取也是采用同样的方法,先求出窗口内像素的灰度共生矩阵,然后求平均相关性特征和能量特征,用这三个特征来代表整个窗口内图像像素的特征信息,将这三个特征值进行标准化处理作为GA-BP网络的数据输入,然后分别对采用灰度特征值和融合特征值作为输入的图像进行分类,并且进行评价。结果表明采用改进的GA-BP网络对融合特征值的网络输入,分类结果的精度和分类速度都高于对单特征的分类效果。更多还原

【Abstract】 Remote sensing technology is new scientific detection technology. It is a realization of the goal of long-range, non-contact measurement, target acquisition and analysis of a technology. Along with the computer and the development of space technology, all kinds of resources and environment the satellite launch and successful operation, through satellite remote sensing the height of the earth from space to panorama and the dynamic changes of various resources such as surface information extraction technology obtained fast development. Therefore, the remote sensing image recognition processing, namely through the extraction of image feature information, and use these features for image classification, to achieve image recognition has been one of the important problems to solve.Neural network for special organization learning and strong from the fault tolerance, and to solve nonlinear mapping problems in liquor unique function.Neural network than traditional statistical parameters classification method borrows a lot of advantage, still can have certain anti-noise ability, by widespread application in pattern recognition and image processing, and etc. According to have used in remote sensing image processing of neural network system, this paper on the application of the most extensive BP neural network are introduced in this paper. Due to the standard BP algorithm in the slow rate of convergence and the dependence of the initial parameters of the network image classification, it is easy to fall into the local minimum value problems. According to the weaknesses of the BP algorithm, we use the algorithm of the global search ability and strong hereditary genetic to train the BP initial network parameters, not only can Get the optimal chromosomes and improve classification efficiency, but also can solve the local minimum problem. Therefore this paper constructs a method, for remote sensing which is based on GA-BP three-layer neural network.Generally used when dealing with remote sensing image classification is a classification method based on spectral characteristics, namely, gray feature for remote sensing image classification, using the single-feature classification accuracy is not high, so this paper is based on spectral and texture features integration of the classification. To the extraction of the spectrum characteristics, using the method of extracting average gray feature. Also use the same method for texture feature extraction, first calculate the GLCM of pixels in the window, and then calculate the average correlation characteristics and energy characteristic,and using three characteristics to represent the characteristic information of the image pixels within the entire window. After that, we make these three eigenvalues be normalized as the GA-BP network data input; and we classify and evaluate the image which make use of gray feature value and integration of the characteristic value as the input. The results show that using the improved GA-BP network integration of the eigenvalue of the network input, the classification accuracy and classification speed is higher than the classification results of single-feature. Figure24table15reference44更多还原