ASTER数据的自组织神经网络分类研究

传统的遥感数据分类方法大多基于统计学的参数估计，假设数据分布服从高斯正态分布。神经网络方法无需参数估计和统计假设，因而，近来越来越多地应用于遥感数据分类之中。介绍了基于聚类分析的自组织特征映射分类方法。ASTER卫星数据是新型遥感数据，包括 3个15 m分辨率波段和 3个30 m分辨率的短波红外波段。选择北京地区的ASTER数据作为方法实验数据，首先对数据进行了小波融合，然后进行了土地覆盖类型的自组织特征映射神经网络分类研究，把研究结果同最大似然判别法得到的分类结果进行了比较，分类精度比最大似然判别法总体提高了9％。

STUDY ON ASTER DATA CLASSIFICATION USING SELF-ORGANIZING NEURAL NETWORK METHOD

    The assumption of statistical model is not needed for Neural Networks (NN) while most traditional classification method for remote sensing data assumed normal distribution model. More and more NN application cases have been found in remote sensing data classification. In this paper, we proposed a method of Kohonen Self-organizing feature map based on clustering analysis. ASTER data is a new remote sensing data, which includes 3 bands of 15 m resolution and 3 bands of 30m resolution. ASTER data of Beijing have been chosen for our research. The land cover classification result in neural networks method has been shown in this paper after wavelet fusion of data. The classification has 9% of accuracy ratio more than MLH classification.
    The idea of neural networks came from the basic structure of functioning of the human brain. In the modern field of science and engineering, the neural networks have strengthened their importance with numerous applications ranging from pattern recognition, fields of classification etc. There are different kinds of the neural networks available depending on the task to be performed. In this study the Kohonen self-organized network is used. There are 6 notes in import layer of the structure of Kohonen self-organized network and ASTER data bands 1,2,3N,5,7,9 corresponding to one note in import layer. Output layer has the structure of 25×25 neural notes. Learning speed α starting value is 0.9, α reduced to 0.001 stopped with net calculation processing. Maximum circulation time is 2 500.  
    ASTER is the only instrument to fly on the EOS AM-1 plate form that will acquire high-resolution image. The primary goal of the ASTER mission is to obtain high-resolution image data in 15 channels over targeted areas of the Earth's surface, as well as black-and-white stereo images, with a revisit time between 4 and 16 days. Band 1、2 are visible bands, band 3N,3B are near inferred bands, the resolution is 15 m; Band from 4 to 9 are group of  short wave inferred bands, theresolution is 30 m; Band from 10~14 are thermal bands, the resolution is 90m. With ASTER's merits earth scientists to address a wide range of globule-change topics. In the paper we introduce Kohonen self-organized network in classification of land cover in Beijing area in 2001 by using ASTER data.