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本文利用多种不同比例尺的遥感图像,分析了陕北地区的地貌特征及其区域分异规律,将陕北地区分为三个地貌区,即东部黄土基岩丘陵区、中部黄土丘陵区和西部风沙区；还分析了地貌条件对农业生产的影响及地貌的区城变化所引起的农业生产布局的区域变化,在此基础上对陕北地区的农业生产布局提出了初步的看法。 相似文献
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本文以利用试验区TM影象为例,介绍了TM影象数据精加工处理的原理、方法和精度。TM影象经计算机精加工后,可以达到1:10万比例尺地形图的平面点位精度要求。本文还介绍了非遥感数据与遥感数据数字复合的原理和工作流程。多种数据复合有利于提高目视判读、自动识别分类、野外取样和定位的精度和速度。 相似文献
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一、前 言遥感数字影象的计算机辅助纠正技术是遥感数字图象处理技术中的一个部分，数字影象的几何纠正在遥感技术中的地位主要表现在：对不同光谱域、不同时域及不同传感方式所获取的影象进行几何配准，以提高计算机自动分类精度或实现对环境的变化监测；作为地球资源和环境调查的结果，需要在几何上得到改正的主题图；制作或更新小比例尺地图。 相似文献
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用ANUDEM建立水文地貌关系正确DEM的方法研究 总被引：4，自引：0，他引：4
针对区域尺度的径流、水土流失定量评价和植被适宜性评价等研究工作需要,利用1∶25万数字地形图和ANUDEM软件,对黄土丘陵区中等分辨率水文地貌关系正确DEM建立方法进行了研究。结果表明该方法所建立的DEM,可以正确反映地貌梁、沟结构及其与流水线网络的关系,对地形描述的能力优于TIN方法建立的DEM;利用ANUDEM和1∶25万地形图插值建立黄土丘陵区DEM的三个主要参数分别为分辨率50或100,计算迭代次数40,第二糙率系数0.8。 相似文献
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大比例尺DEM更能够清晰详细地表述地面高程信息,它在测绘、水文、气象、地貌、地质、土壤、工程建设、通讯、气象、军事等国民经济和国防建设以及人文和自然科学领域有着广泛的应用。针对大比例尺DEM的制作精度要求比较高、制作成本高的特点,本文提供大比例尺数字高程模型DEM制作方法的一些经验和体会。 相似文献
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本文介绍生成正射像片的一种新方法。它采用的一种数字地形模型，由栅格数据和矢量数据两者组成，如断裂线。这种方法克服了在大比例尺地图中由于简单栅格ＤＴＭ的精度不高而引起的地貌移位问题。 相似文献
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陈军 《武汉大学学报(信息科学版)》1984,(1)
本文着重介绍在遥感影象分类中应用数字地面模型(DTM)改正地物反射光谱中地形影响的方法和试验。笔者选择了位于湖北和江西两省交界处的九宫山地区做为试验区域。试验结果表明,在山地区,单纯利用原始Landsat MSS影象分类,效果很差;采用辐射校正分类法和附有太阳入射角数据的MSS影象分类方法,都能够不同程度地改正地形对地物反射光谱的影响,从而提高了Landsat—2 MSS影象的分类精度。其中附有太阳入射角数据的MSS影象分类精度达到70.4％。 相似文献
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讨论了运用阿尔玛日宇宙飞船在不同轨道所拍摄的重叠雷达影象来建立地球表面数字地形模型的方法，以及运用立体像对制作一张地表地形的三维影象数字图象的方法及处理过程的算法，运用与美国航天飞机成像雷达－Ｂ系统相像的北美大盆地沙漠影象，介绍了从两张重叠影象的处理中获取正射地形图影象的方法。 相似文献
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基于ETM＋制作土地利用覆盖图——以制作北京1：5万土地利用覆盖图为例 总被引：3，自引：1，他引：2
简要介绍了基于LANDSAT7 ETM＋影像,采用计算机非监督分类、监督分类与人工解译相结合的方法制作土地利用覆盖图的过程和所采用的关键技术,给出了适用于规模化生产土地利用覆盖数据的工艺流程图。使用该方法制作的十一种分类要素的北京地区1：5万土地利用覆盖图,平均分类精度为84．85％,可以满足一般用户对土地利用覆盖图的要求。 相似文献
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Inderjit Singh 《Journal of the Indian Society of Remote Sensing》1988,16(4):43-52
LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation. Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map. Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.) 相似文献
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Manfred Ehlers Monika Ghler Ronald Janowsky 《ISPRS Journal of Photogrammetry and Remote Sensing》2003,57(5-6):315
The advent of very high-resolution satellite programs and digital airborne cameras with ultra high resolution offers new possibilities for very accurate mapping of the environment. With these sensors of improved spatial resolution, however, the user community faces a new problem in the analysis of this type of image data. Standard classification techniques have to be augmented with appropriate analysis procedures because the required homogeneity of landuse/landcover classes can no longer be achieved by the integration effect of large pixel sizes (e.g., 20–80 m). New intelligent techniques will have to be developed that make use of multisensor approaches, geographic information system (GIS) integration and context-based interpretation schemes.The ideal goal should be that GIS ‘intelligence’ (e.g., object and analysis models) should be used to automate the classification process. In return, GIS objects can be extracted from a remote sensing image to update the GIS database. This paper presents the development of an automated procedure for biotope type mapping from ultra high-resolution airborne scanner data (HRSC-A). The hierarchical procedure incorporates a priori GIS information, a digital surface model (DSM) and multispectral image data. The results of this study will serve as a basis for a continuous environmental monitoring process in the tidally influenced region of the Elbe River, Germany. 相似文献
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We developed a classification workflow for boreal forest habitat type mapping. In object-based image analysis framework, Fractal Net Evolution Approach segmentation was combined with random forest classification. High-resolution WorldView-2 imagery was coupled with ALS based canopy height model and digital terrain model. We calculated several features (e.g. spectral, textural and topographic) per image object from the used datasets. We tested different feature set alternatives; a classification accuracy of 78.0% was obtained when all features were used. The highest classification accuracy (79.1%) was obtained when the amount of features was reduced from the initial 328 to the 100 most important using Boruta feature selection algorithm and when ancillary soil and land-use GIS-datasets were used. Although Boruta could rank the importance of features, it could not separate unimportant features from the important ones. Classification accuracy was bit lower (78.7%) when the classification was performed separately on two areas: the areas above and below 1 m vertical distance from the nearest stream. The data split, however, improved the classification accuracy of mire habitat types and streamside habitats, probably because their proportion in the below 1 m data was higher than in the other datasets. It was found that several types of data are needed to get the highest classification accuracy whereas omitting some feature groups reduced the classification accuracy. A major habitat type in the study area was mesic forests in different successional stages. It was found that the inner heterogeneity of different mesic forest age groups was large and other habitat types were often inside this heterogeneity. 相似文献
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Pravin D. Kunte 《Journal of the Indian Society of Remote Sensing》1988,16(2):59-66
Digital sensing systems, on board remote sensing satelites, have provided a very powerful tool for conducting earth resources studies using digital computers. This paper mainly describes various digital techniques that are applied on Remotely Sensed data to extract various landuse features and to identify, broadly, geological rock types using Dipix Image Processing System. A variety of image enhancement algorithms are used to delineate four major Geological groups and several landuse features such as drainage, river, canal, vegetational pattern, railways, road etc. Some of the landuse features have been of help in identifying and marking of four major geological groups namely Lower pre-Cambrian rocks, Upper and Lower Siwaliks and recent alluvial deposits. 相似文献
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Integration of spatial and spectral information is an effective way in improving classification accuracy. In this article a new framework, based on multi-scale spatial weighted mean filtering (MSWMF) and minimum spanning forest, is proposed for the spectral–spatial classification of hyperspectral images. In the proposed framework, at first the image is smoothed by MSWMF and then the first eight principal components are extracted. Using support vector machine, at each scale of MSWMF, a classification map is produced in order to generate a marker map in the next step. Then, the minimum spanning forest is built on the marker map. Finally, in order to create a final classification map, all the classification maps of each scale are merged with a majority vote rule. The experimental results of the hyper-spectral images indicate that the suggested framework enhances the classification accuracy, in comparison with previously classification techniques. So, it is interesting for hyperspectral images classification. 相似文献
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基于GIS的中国东北植被综合分类研究 总被引：53，自引：3，他引：50
ＮＯＡＡ／ＡＶＨＲＲ由于运行周期短、覆盖范围大、成本低、波段宽等特点,目前正越来越广泛地受到人们的普遍关注。在大尺度、中尺度植被遥感上,ＮＯＡＡ／ＡＶＨＲＲ具有陆地卫星无法比拟的优势,但在另一方面,ＮＯＡＡＡＶＨＲＲ也存在分辨率低、数据变形较大和几何畸变较严重等问题。这样,在应用ＮＯＡＡＡＶＨＲＲ数据进行大区域植被制图时,植被分类的精度仍待提高。本文从理论上探讨了将地理信息系统提供的地理数据与遥感数据复合的可行性;尝试在ＧＩＳ环境下,将气温、降水、高程３个影响区域植被覆盖的主要指标,按一定的地面网格系统和数学模式进行量化,生成数字地学影像,并使之与经过优化、压缩处理的ＮＯＡＡＡＶＨＲＲ数据进行复合,对复合后的综合影像进行监督分类。分类结果显示,与传统的应用最大似然分类方法对单一遥感图像分类相比,该综合分类方法分类精度提高了１８．３％,该研究方法改变了遥感影像的单一信息结构;丰富了图像的信息含量;完成了地理数据的数字传输、处理、存储及影像化显示。 相似文献
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基于特征尺度分布与对极几何约束的高清影像快速密集匹配方法 总被引：1，自引：1，他引：0
本文重点阐述了基于机器视觉的智能摄影测量的效率基础问题之三：高清影像快速智能匹配处理。图像特征匹配是影响数字摄影测量坐标空间计算效率的基础数据处理过程。为了解决高分辨率数据匹配校验计算成本更高及相似特征干扰等影像产品生成效率负面影响问题,本文通过研究影像尺度不变特征的数学本质,结合多视图相机几何模型,推导并验证了图像特征点的降采样尺度分布规律。根据图像空间几何关系在降采样尺度上的匹配映射关系,缩减图像匹配过程中的计算量并筛选有效待匹配点集,将特征点数量105量级的快速全局特征距离初匹配时长限制在亚秒级。在此基础上结合特征尺度分布信息改进的对极几何约束,改进特征匹配算法,辅助缩小匹配搜索范围,通过特征索引与分区并行处理,实现高清影像同名特征的高精度快速密集匹配,提升特征点基数、匹配特征点对数量与正确率。本文使用intel i7-4720HQ与NVIDIA GTX970M进行试验,基于尺度分布特性的特征匹配方法,以亚秒级的计算时间,获取符合多约束条件的103量级的匹配点对,为数字影像的快速高精度处理提供了一种新思路,在充分满足数字摄影测量的精度的基础上可提高其产品生成效率。 相似文献
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多尺度分析技术广泛应用于高分辨率遥感影像的特征提取和建模。分解层数受制于影像的大小,下采样小波变换实现的影像多尺度表达难以描述大范围的空间模式,导致分类结果出现"胡椒盐"现象;面向对象的影像分析技术虽避免了"胡椒盐"现象,但由于仅利用了单尺度的的特征,也难以描述影像多层次的空间模式,导致分类精度较低。为改善分类结果中的"胡椒盐"现象和提高分类精度,提出了一种结合区域多尺度遥感影像分割和马尔可夫随机场的分类方法。首先,获得原始影像过分割区域,依据区域内亮度均值以及区域间的共享边界长度信息,提取影像低频和高频特征,采用该低频特征波段代替原始影像,重复分割与特征波段提取过程,形成影像的区域多尺度表达。然后,以原始图像为初始尺度,以分割区域为处理单元,以更细尺度分类结果为标记场先验,以当前高频特征建立特征场,逐层分类、投影,获得最终尺度分类结果。合成纹理影像和多光谱遥感影像的实验表明:相比于小波域多尺度建模方法和单尺度区域建模方法,本文提出的方法可以有效提高分类精度,并避免"胡椒盐"现象的产生。 相似文献
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Abstract Artificial neural networks (ANN) have recently been popularly used in image classification. Input features to most ANNs are extracted based on a one class per pixel basis. This requires a large number of training samples and thus a slow training rate. In this paper, we describe the use of a windowing technique to extract textural features such as average intensity, second moment of intensity histogram and fractal surface dimension from an image. This method of image characterization reduces the number of training samples efficiently, yet retains a reasonable overall classification accuracy. The ANN is trained based on the back‐error propagation algorithm. The method is applied for landuse classification of Synthetic Aperture Radar (SAR) images. An example is given for a site in Kedah State, Malaysia. The SAR images (HH,HV,VV) were taken by the Canadian Centre for Remote Sensing (CCRS) CV‐580 airborne C‐band SAR system in November 1993 during their GlobeSAR mission in Malaysia. These multi‐polarization SAR images are co‐registered with a Landsat Thematic Mapper (TM) channel 5 image from same area. An overall classification accuracy of about 86.95% is achieved using windowing technique, as compared to 68.22% based on one class per pixel approach. This shows that through fractal and textural information, the windowing technique when applied in an ANN classifier has a great potential in remote sensing applications. 相似文献