An optimal selection of probability distribution functions for unsupervised land cover classification of PALSAR-2 data

Different types of classification techniques are available in the literature for the classification of Synthetic Aperture Radar (SAR) data into various land cover classes. Various SAR images are available for land cover classification such as ALOS PALSAR (PALSAR-1, PALSAR-2), RADARSAT and ENVISAT. In this paper, we have attempted to explore probability distribution function (pdf) based land cover classification using PALSAR-2 data. Over 20 different statistical distribution functions are analyzed for different classes based on statistical parameters. Probability distribution functions are selected based on Chi-squared goodness of fit test for each individual class. A decision tree based classifier is developed for classification based on the selected pdf functions and its statistical parameters. The proposed classification approach has an accuracy of 83.93%.     

电子公文的全文检索系统构建

概述了全文检索技术特点,介绍了MicrosoftIndexServer站点索引工具的原理,及本系统采用的文件格式。     

关于航空发动机结构声疲劳寿命估算方法的探讨

噪声载荷是一种高频、宽带随机载荷,在低循环疲劳寿命预测中被广泛采用的时域计数法很难直接利用。根据现有的累积疲劳损伤理论,噪声载荷下结构疲劳寿命分析的关键在于如何在频域中获得雨流循环的概率密度函数。本文对从疲劳应力功率谱密度出发获得寿命估算所需要的应力峰值或应力雨流循环幅值的概率密度函数的方法进行了分析与比较,并对发动机结构声疲劳分析中值得深入研究及发展的方法给出了建议。