基于遥感影像理解模型的土地利用时空变化监测——以龙口市为例     

徐秋晓  于明洋  崔健 《测绘科学》2008,33(2):113-116

本文从遥感影像理解模型出发,探讨了遥感信息的知识发现及特征提取问题,特别针对士地利用研究领域,提出了纳入GIS信息的遥感影像综合理解模型,将GIS数据(主要包括高程和坡度)与遥感影像结合起来,从中发现知识,并将知识用于遥感影像的专题信息提取,建立了地学规律知识库,从而提高了遥感影像的分类精度。本文基于遥感影像理解模型,发现土地利用变化区域,提取变化信息,动态地监测了龙口市土地利用的时空变化情况。  相似文献   

Excel VBA在测量数据处理中的应用   总被引：2，自引：0，他引：2  

王汉雄  乔景顺 《测绘科学》2008,33(2):210-212

Excel是office办公软件中的一个组件,而VBA是office应用程序共享的一种通用的自动化语言,可以使常用的office应用程序自动化,并可以创建自定义的解决方案。本文对Excel2003的数据处理功能进行分析,论述通过Excel2003的VBA编辑器创建"宏"功能实现测量数据处理的优点;并结合道路工程中桩坐标及其放样数据计算实例,给出了通过VBA编程进行测量数据处理的方法。  相似文献   

基于LBV变换的遥感影像多步骤分类法研究   总被引：1，自引：0，他引：1  

彭笃明  张安定  李德一  米姗姗 《测绘科学》2008,33(3):56-58

面对与日俱增的遥感数据,如何快速准确地进行影像分类成为遥感领域亟待解决的问题。本文在前人的基础上提出了一种基于LBV变换的遥感影像多步骤分类方法。L:地物的总辐射水平,集中反映了裸地的信息;B:地物的可见-红外光辐射平衡,是地面水分状况和水体存在的一个良好指标;V:地物辐射随波段变化的方向和速度向量,能集中反映地面植被状况。多步骤分类法遵循由易到难的原则,能克服类别之间的互相影响,从而提高分类精度。文章结合LBV变换和多步骤分类法的优点对影像进行了分类,结果表明该方法简单易行,且能达到良好的分类精度。  相似文献   

遥感图像线性影纹理解专家系统设计与实现     

李百寿  秦其明  许军强  张万峰  姚云军 《测绘科学》2008,33(2):167-169,109

线性影纹信息是遥感图像中的一类重要信息,线性影纹信息自动提取是遥感图像智能解译的重要研究领域。笔者采用基于特征对象的专家系统技术来完成线性影纹信息的简单类别提取,设计并实现了线性影纹理解专家系统;阐明了系统结构,解译规则获取、表达,基于消息的不确定性推理机及解释机的设计;最后利用系统做了线性影纹类别提取实验,结果表明基于特征对象的专家系统用于线性影纹类别自动提取是切实可行的,并具有较高的提取精度。  相似文献   

Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps     

R.P. Gupta  D.P. Kanungo  M.K. Arora  S. Sarkar 《International Journal of Applied Earth Observation and Geoinformation》2008

Evaluation of maps generated from different conceptual models or data processing approaches at spatial level has importance in many geoenvironmental applications. This paper addresses the spatial comparison of different landslide susceptibility zonation (LSZ) raster maps of the same area derived from various procedures.  相似文献   

分形理论在云南个旧锡矿遥感信息定量化分析中的应用   总被引：2，自引：0，他引：2       下载免费PDF全文

张建东  彭省临  杨斌  刘明 《地质找矿论丛》2008,23(2):157-160,164

应用分形理论和方法对个旧锡矿遥感信息进行了定量化研究.结果显示,在格子边长为1.4～4.5 km的标度范围内,其遥感蚀变和断裂构造进入的总格子数的对数与格子边长的对数均具有很好的线性相关性,表明在研究标度范围内,各子区遥感线性与蚀变信息具有良好的统计自相似性和分形特征.在此基础上,以19号区和26号区为例,分析并进一步探讨了遥感信息分形特征的成矿意义.  相似文献   

Using spectral information from the NIR water absorption features for the retrieval of canopy water content     

J.G.P.W. Clevers  L. KooistraM.E. Schaepman 《International Journal of Applied Earth Observation and Geoinformation》2008

Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral information related to the water absorption features at 970 nm and 1200 nm offers possibilities for deriving information on CWC. In this study, we compare the use of derivative spectra, spectral indices and continuum removal techniques for these regions. Hyperspectral reflectance data representing a range of canopies were simulated using the combined PROSPECT + SAILH model. Best results in estimating CWC were obtained by using spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features. Real data from two different test sites were analysed. Spectral information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970 nm can best be used for estimating CWC.  相似文献   

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China   总被引：3，自引：0，他引：3  

Jianqiang Ren  Zhongxin Chen  Qingbo Zhou  Huajun Tang   《International Journal of Applied Earth Observation and Geoinformation》2008,10(4):403

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.  相似文献   

The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia     

S. Fritz  M. Massart  I. Savin  J. Gallego  F. Rembold   《International Journal of Applied Earth Observation and Geoinformation》2008,10(4):453

Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different crop types and achieved satisfactory and good results for winter crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.  相似文献   

A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis   总被引：2，自引：0，他引：2  

Yu Liu  Qinghua Guo  Maggi Kelly   《ISPRS Journal of Photogrammetry and Remote Sensing》2008,63(4):461-475

Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis.  相似文献