【作者】 崔曦；

【导师】 张春森；

【作者基本信息】 西安科技大学 ， 地图制图学与地理信息工程， 2008， 硕士

【摘要】 随着多波段,多分辨率遥感数据的增加和应用质量的不断提高,需要开发更加有效的遥感数据分类算法,当前图像处理软件中的统计分类方法是以参数假设为约束条件,需要数据分布服从高斯分布。但多数的遥感数据的分布并不服从高斯分布的假设条件,因而导致分类精度不高的原因之一。因此,本文以非线性理论为指导,探索了以SOFM神经网络分类方法,改进的BP神经网络和模糊数学方法的遥感数据分类方法的实现。本文以广州三水市Landsat TM5,4,3三波段遥感影像和SPOT遥感影像为数据源,主要研究了神经网络和模糊算法两种方法在遥感图像分类中的应用。根据所选区域的实际情况,将土地覆盖/利用类型分为五类:水体(包括河流,湖),旱地,裸露地(包括道路,居民地,桥梁和未利用地)、林地和水田5类覆盖物进行分类试验研究。在基于神经网络分类实验中,我们采用了自组织特征映射神经网络(SOFM)算法和改进的BP算法方法对图像进行了分类,并与传统的最大似然法比较达到了很好的效果。在基于模糊网络方法的分离实验中,我们分别采用了K-均值算法(一种硬划分聚类方法)、模糊C-均值算法(等轴空间聚类)对图像进行了分类,并进行了对比分析,结果显示模糊方法精度较好。更多还原

【Abstract】 With the increase of multi-band and multi-resolution remote sensing data, more effective algorithms are needed to improve the classification precision of the new remote sensing data. Traditional supervised classification methods require the assumption that data distribution obeys Gauss normal distribution, however, actual remote sensing data distributions do not always obey Gauss normal distribution, which is one of the reasons of low precise classification by using traditional methods. Therefore, the perspective of the dissertation is to select some nonlinear method and to achieve combined results. The basic elements used in combined methods are SOFM Neural Network Classification, BP Neural Network Classification and Fuzzy Mathematics. The algorithms are realized by programing.This paper takes the Landsat TM5,4,3 and SPOT Remote Sensing image of Guangdong Sanshui as source data. Its major tasks are to research Neural Network and Fuzzy Mathematics using in Remote Sensing image classification. According to the actual situation of the selected areas, we divide the land cover/use into five classes: artificial land(cities and towns,traffic land,mine project land), paddy field, arid land, woodland(forest,lawn, virescence land) ,water. In the experiment based on Neural Network Classification s, we use SOFM Neural Network and BP Neural Network Classification, and have a good result from comparing the result with the result of Maximum likelihood classification. In the experiment based on Fuzzy Mathematics,We analyze and compare the result of fuzzy classification with the result of Hard C-Means Clustering Method ,we can see that it has a good result and higher precision to use fuzzy technique classify remote sensing image.更多还原