Development of Analysis Processing System Using Orbital Satellite Image Data

对从轨道卫星上的无源传感器提取的图像数据进行处理的SAAS系统进行了改进和改造。这个创新的IBASYS系统的内容如下：(1)将原来的FORTRAN程序翻译成C++，使之能够在工作站和个人计算机上运行。(2)为了验证分析准备所需的数据条件，将颜色模式从7个波段扩展到18个波段。(3)为了提高图像几何变换的处理效率，分层次增加了误差分析和图像变换处理的人机处理。(4)在无监督分类中增加了K-均值方法，提出了一种利用栅格分类处理的算法。(5)增加了用于验证所选训练数据在图像数据上的应用的一般概念算法。(6)作为分类方法的扩展，对模糊神经网络分类进行了理论研究，将模糊理论和神经网络理论相结合，并将其用于轨道卫星图像数据的分类。

The SAIAS system that processes image data extracted from passive sensors onboard in orbital satellites is improved and renovated. The contents of this innovated IBASYS system is described below :(1) The original FORTRAN programs are translated to C++, making possible to run on workstations and personal computers.(2) To verify the data conditions necessary for analysis preparation, the color pattern is expanded from 7 to 18 bands.(3) To improve the processing efficiency of geometric transformation of an image, the man-machine processing of error analysis and the image transformation processing was hierarchically added.(4) The K-means method is added in the unsupervised classifications, developing an algorithm using a raster classification processing.(5) A general notion algorithm to verify the application of a selected training data on an image data is added.(6) As an expansion to the classification methods, a theoretical investigation is made on FNN classification, a combination of fuzzy theory and neural network theory, and is made possible to classify an orbital satellite image data.