针对非地理信息系统专业“地理信息系统基础”课程设置的特点，在控讨CAI教学优越性的基础上，分析CAI课件的体系结构与内容，提出了系列化课件开发思想，包括教学课件、自学课件、实验课件、网络课件、演示课件、测试与考核课件，在素材资源共享的基础上实现系列课件的结合与配套使用，以适应不同用户、不同教学环节的要求，促进地理信息系统教学实践与教学改革的发展。

Taking into account the demands of hyperspectral remote sensing (RS) image retrieval and processing, some encoding methods of spectral vector including directencoding, feature-based encoding and tree-based encoding methods are proposed and compared. In direct encoding, based on the analysis of binary encoding and quadvalue encoding, decimal encoding is proposed. It is proved that quadvalue encoding and decimal encoding are suitable to fast processing and retrieval. In absorption feature-based encoding method, five common metrics are compared. Because locations of reflection Pabsorption features are sensitive to noise, this method is not very effective in retrieval. In tree-based encoding methods, bitree, quadtree, octree and hextree are proposed and iscussed. It is proved that 2-level octree and 2-level hextree are more effective than bitree and quadtree. Finally, quadvalue encoding, decimal encoding, 2-level octree and 2-level hextree are proposed in spectral vectors encoding, similarity measure and hyperspectral RS image retrieval.

采矿塌陷地动态监测是工矿区资源管理与环境保护的重要方面，遥感技术可在其中发挥重要作用，从遥感图象中提取采矿塌陷地是遥感应用于矿山资源环境监测的重要研究课题。传统的提取方法主要基于光谱特征，精度与效率都难以满足应用要求，为了以较高的精度，从遥感图象中提取塌陷地，必须建立新的方法与模型。将遥感技术与GIS相结合进行专题信息提取是有效的途径之一。本文根据研究区的特点，以具体应用为指导，遥感技术与GIS相结合，提出了GIS支持下的分层分类、基于GIS变化区域识别的分类、基于GIS和领域知识对遥感分类图象进行后处理、GIS支持下采矿塌陷地的直接提取等方法与模型，充分应用光谱特征、地学特征与信息、领域和专家知识及其他统计数据辅助进行遥感图象处理与专题信息提取。这些方法在精度、效率等方面均较传统方法有较大提高，最大提取精度可达到89%， 能够有效地对工矿区土地塌陷态势进行动态监测。

在分析SAR图象斑点噪声特性和主要滤波算法的基础上，将中值滤波、均值滤波、LEE与增强LEE滤波、FROST与增强FROST滤波、KUAN滤波、GAMMA滤波、小波软阈值滤波等算法应用于RADARSAT图象滤波，以平滑指数和边缘保持指数等作为评价指标，对各种方法进行了比较。结果表明，中值、均值滤波算法的效果较差，而各种自适应滤波算法和小波软阈值算法是最有效的。

In this paper, two new similarity measure methods based on set theory were proposed. Firstly, similarity measure of two sets based on set theory and set operation was discussed. This principle was used to spectral vectors, and two approaches were proposed. The first method was to create a spectral polygon corresponding to spectral curve, and similarity of two spectral vectors can be replaced by that of two polygons. Area of spectral polygon was used as quantification function and some effective indexes for similarity and dissimilarity were computed. The second method was to transform the original spectral vector to encoding vector according to absorption or reflectance feature bands, and similarity measure was conducted to encoding vectors. It proved that the spectral polygon-based approach was effective' and can be used to hyperspectral RS image retrieval.