基于多种子点模糊连接度的SPOT影像道路提取

结合模糊连接度理论和SPOT影像上道路的表现特性提出了主干道路半自动提取的方法。首先,对原始影像进行去噪滤波处理,再人工选取能够代表道路特性的种子点,计算出各个像素点相对种子点的模糊连接度,将模糊连接度值大于一定阈值的像素点提取出来,从而得到道路线的支持区域;接着对二值道路域细化抽去得到道路中心线;实验证明,该方法可以取得较好的效果。     

卫星遥感技术的发展和最新成就

本文概述 1972～ 1999年卫星遥感发展情况及其在地球科学中的应用。重点在于介绍它的最新成就 ,如美国陆地卫星第 7号 (L ANDSAT- 7)和加拿大雷达卫星第 1号 (RADARSAT- 1) ,它们代表卫星遥感的最新发展水平。     

SPOT数据反演地物辐射亮度和反射率的基础研究

以往用户对于SPOT卫星数据的应用主要针对其高空间分辨率的特性,但是因SPOT卫星传感器的波段数和波长范围有限,故利用SPOT数据进行定量遥感分析(如地物辐射亮度及反射率)的应用研究较少。然而,利用SPOT数据进行地物辐射亮度和反射率的测量也有其高空间分辨率的优势,可以更精细地分辨各类地物的细节,降低混合像元对于定标分析的影响,提高定量遥感分析的准确度。本文将就SPOT数据的辐射校正原理及其在反演地物辐射亮度和反射率方面的应用开展基础性研究,对于SPOT卫星数据的定标参数读取提供了相应的方法,选取了几种比较典型的地物进行数据分析,并利用Landsat-7数据作为参考,评价SPOT数据测量地物辐射参数的可靠性和应用效果。     

Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data

Integrating multiple images with artificial neural networks (ANN) improves classification accuracy. ANN performance is sensitive to training datasets. Complexity and errors compound when merging multiple data, pointing to needs for new techniques. Kohonen's self-organizing mapping (KSOM) neural network was adapted as an automated data selector (ADS) to replace manual training data processes. The multilayer perceptron (MLP) network was then trained using automatically extracted datasets and used for classification. Two hypotheses were tested: ADS adapted from the KSOM network provides adequate and reliable training datasets, improving MLP classification performance; and fusion of Landsat thematic mapper (TM) and SPOT images using the modified ANN approach increases accuracy. ADS adapted from the KSOM network improved training data quality and increased classification accuracy and efficiency. Fusion of compatible multiple data can improve performance if appropriate training datasets are collected. This proved to be a viable classification scheme particularly where acquiring sufficient and reliable training datasets is difficult.     

Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery

To have sustainable management and proper decision-making, timely acquisition and analysis of surface features are necessary. Traditional pixel-based analysis is the popular way to extract different categories, but it is not comparable by the achievements that can be achieved through the object-based method that uses the additional characteristics of features in the process of classification. In this paper, three types of classification were used to classify SPOT 5 satellite image in mapping land cover; Support vector machine (SVM) pixel-based, SVM object-based and Decision Tree (DT) pixel-based classification. Normalised Difference Vegetation Index and the brightness value of two infrared bands (NIR and SWIR) were used in manually developed DT classification. The classification of the SVM (pixel based) was generated using the selected groups of pixels that represent the selected features. In addition, the SVM (object based) was implemented by using radial-based function kernel. The classified features were oil palm, rubber, urban area, soil, water and other vegetation. The study found that the overall classification of the DT was the lowest at 69.87% while those of SVM (pixel based) and SVM (object based) were 76.67 and 81.25%, respectively.     

变窗口纹理在SPOT5影像土地覆盖分类中的应用研究

纹理作为有效的空间信息,在遥感信息提取中有着广泛的应用,其中纹理窗口的选择对分类精度影响较大,而且遥感影像中不同地物类型具有多尺度特性,利用单一窗口纹理信息进行分类不能使得各地物类型同时达到较高精度.该文采用SPOT5影像对研究区进行长序列窗口纹理分类实验,并提出变窗口纹理分类方法,实验区分类实验及精度对比表明:长序列窗口纹理分类实验中,7×7窗口分类精度最高,但在这一窗口下建设用地、耕地、裸地等地类的精度仍低于总体精度;而采用基于长序列窗口纹理分类后验概率的变窗口纹理分类方法,总精度达到86.17％,Kappa系数达到0.8230,与7×7窗口分类精度相比各地物类型的生产者精度与用户精度都有进一步提高,验证了变窗口纹理分类方法的可行性.     

一种以FCD模型从SPOT影像提取植被覆盖率的方法

论及以FCD模型原理提取植被覆盖率的方法;同时,针对SPOT影像的波段特征,重新构造了相关指数:植被指数(VI)、裸土指数(BI)和阴影指数(SI),并在ERDAS软件系统上对该方法进行了实验。然后,通过野外实地考察,应用 GPS、数码相机等进行实地采点,并对该方法得出的结果进行了验证。结果表明,总体精度达到了80％以上,说明该方法具有较高的提取精度,能满足大中尺度的植被覆盖率调查要求。     

SPOT 5卫星影像在广东省土地资源动态监测中的应用

介绍了在土地资源监测管理中,利用SPOT 5卫星影像进行正射纠正、全色波段与多光谱数据融合、模拟自然彩色的工艺流程。     

西安市SPOT2．5m卫星影像数据处理的实践与探讨

本文结合西安市SPOT2.5m卫星影像数据处理项目,探讨了一种SPOT5卫星影像数据处理的方法。     

SPOT立体图像的有限元最小二乘匹配

本文将多点最小二乘匹配算法扩展为有限元最小二乘匹配,导出了有限元最小二乘匹配的数学模型,并提出了自动产生视差格网的方案;通过试验研究了有限元最小二乘匹配中有关参数,例如,辐射变换参数、虚拟观测的权值以及视差格网间隔的确定。有限元最小二乘匹配算法无需预先进行核线重排列,就可以直接答解出有限个格网点的上下视差和左右视差。这种算法亦可用于其他任何投影方式成象的立体图象的自动匹配。