共查询到20条相似文献，搜索用时 31 毫秒
1.
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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There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method.Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified.Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers.Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information. 相似文献
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基于Landsat-8 OLI影像的植被信息提取方法研究 总被引：1，自引：0，他引：1
植被是地理环境的重要组成部分,在城市生态环境系统中扮演着非常重要的角色。本文以池州市2014年OLI遥感影像为基础,结合30 m空间分辨率的DEM数据,在ENVI 5.1和ArcGIS 10.2软件的支撑下,对该区植被信息进行提取。通过对比原始波段组合、主成分分量组合和衍生波段组合的分类精度,确定植被信息提取的最佳波段组合,并对植被提取结果进行精度验证。结果表明:考虑NDVI、绿度指数和第一主成分的衍生波段组合植被提取精度最高,与该区已知的土地利用类型中的植被覆盖度进行比较,精度达到89.16%。这说明该波段组合方案对于Landsat-8影像提取植被信息效果较好,可以为其他地区植被信息的提取提供参考。 相似文献
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Reliable and up-to-date urban land cover information is valuable in urban planning and policy development. Due to the increasing demand for reliable land cover information there has been a growing need for robust methods and datasets to improve the classification accuracy from remotely sensed imagery. This study sought to assess the potential of the newly launched Landsat 8 sensor’s thermal bands and derived vegetation indices in improving land cover classification in a complex urban landscape using the support vector machine classifier. This study compared the individual and combined performance of Landsat 8’s reflective, thermal bands and vegetation indices in classifying urban land use-land cover. The integration of Landsat 8 reflective bands, derived vegetation indices and thermal bands overall produced significantly higher accuracy classification results than using traditional bands as standalone (i.e. overall, user and producer accuracies). An overall accuracy above 89.33% and a kappa index of 0.86, significantly higher than the one obtained with the use of the traditional reflective bands as a standalone data-set and other analysis stages. On average, the results also indicate high producer and user accuracies (i.e. above 80%) for most of the classes with a McNemar’s Z score of 9.00 at 95% confidence interval showing significant improvement compared with classification using reflective bands as standalone. Overall, the results of this study indicate that the integration of the Landsat 8’s OLI and TIR data presents an invaluable potential for accurate and robust land cover classification in a complex urban landscape, especially in areas where the availability of high resolution datasets remains a challenge. 相似文献
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基于TM影像的城市建筑用地信息提取方法研究 总被引：2，自引：0，他引：2
本文选用金华市Landsat TM影像为研究的数据源,在归一化裸露指数基础上,利用归一化植被指数提取出非植被信息,通过图像二值化、叠加分析以及掩膜处理去除了低密度植被覆盖区域的噪音信息,自动提取了金华城市建筑用地信息。研究结果表明,归一化裸露指数和归一化植被指数相结合的方法弥补了单一利用归一化裸露指数来提取城市建筑用地信息的不足,提高了提取精度,而且结果客观可信,是一种不经人为干预的、快速有效的提取城市建筑用地方法。 相似文献
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《地理信息系统科学与遥感》2013,50(3):338-359
Computational trends toward shared services suggest the need to automatically manage spatial scale for overlapping applications. In three experiments using high-spatial-resolution optical imagery and LIDAR data to extract impervious, forest, and herbaceous classes, this study optimized C5.0 rule sets according to: (1) spatial scale within an image tile; (2) spatial scale within spectral clusters; and (3) stability of predicted accuracies based on cross validation. Alteration of the image segmentation scale parameter affected accuracy as did synergy with LIDAR derivatives. Within the tile examined, forest and herbaceous areas benefited more from optical and LIDAR synergy than did impervious surfaces. 相似文献
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Estimates of solar radiation distribution in urban areas are often limited by the complexity of urban environments. These limitations arise from spatial structures such as buildings and trees that affect spatial and temporal distributions of solar fluxes over urban surfaces. The traditional solar radiation models implemented in GIS can address this problem only partially. They can be adequately used only for 2‐D surfaces such as terrain and rooftops. However, vertical surfaces, such as facades, require a 3‐D approach. This study presents a new 3‐D solar radiation model for urban areas represented by 3‐D city models. The v.sun module implemented in GRASS GIS is based on the existing solar radiation methodology used in the topographic r.sun model with a new capability to process 3‐D vector data representing complex urban environments. The calculation procedure is based on the combined vector‐voxel approach segmenting the 3‐D vector objects to smaller polygon elements according to a voxel data structure of the volume region. The shadowing effects of surrounding objects are considered using a unique shadowing algorithm. The proposed model has been applied to the sample urban area with results showing strong spatial and temporal variations of solar radiation flows over complex urban surfaces. 相似文献
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国产高分卫星分辨率的不断提高,使其可以从几何形态、纹理结构及光谱信息等不同侧面实现对城市地表要素的精细描述。与面向对象分类技术相比,深度学习技术的快速发展,使得城市建筑物提取的精度不断提高。然而,由于道路两旁高大建筑物及树木的遮挡,城市道路的提取精度依然有限。本文在利用卷积神经网络提取建筑物的基础上,利用OSM面状道路数据及城市边界数据,结合植被指数和水体指数,借助空间图层叠加,使得城市建筑物、道路、植被和水体提取总体精度优于90%,为国产高分影像辅助城市精细化管理和应用提供了有效解决方案。 相似文献
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融合多源遥感数据的高分辨率城市植被覆盖度估算 总被引：2，自引：0，他引：2
准确获取城市植被覆盖定量信息对城市生态环境评价,城市规划及可持续城市发展具有重要意义。遥感技术的发展为获取区域及全球植被覆盖信息提供了有效手段,目前基于单传感器、单时相遥感数据的城市植被覆盖度估算方法得到较为广泛的应用。然而,由于城市地表覆盖的复杂性、植被类型的多样性,在一定程度上影响了城市植被覆盖信息提取的精度。为此,本文提出一种基于多源遥感数据与时间混合分析的城市植被覆盖度估算方法。首先,通过时空融合、植被物候特征分析获得最佳时序的GF-1 NDVI数据;其次,基于时间序列的GF-1 NDVI及Landsat 8 SWIR1、SWIR2数据,采用时间混合分析方法以长沙市为例估算城市植被覆盖度。实验研究表明,基于多源遥感数据与时间混合分析方法获得了较高精度的城市植被覆盖度估算（RMSE为0.2485,SE为0.1377,MAE为0.1889）,相对于单时相光谱混合分析、传统的像元二分法,本文提出的方法更为稳定,在低、中、高不同植被覆盖区均能获得较高的估算精度,为城市植被覆盖度定量估算提供了有效方法。 相似文献
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Airborne laser scanning (ALS) is increasingly being used for the mapping of vegetation, although the focus so far has been on woody vegetation, and ALS data have only rarely been used for the classification of grassland vegetation. In this study, we classified the vegetation of an open alkali landscape, characterized by two Natura 2000 habitat types: Pannonic salt steppes and salt marshes and Pannonic loess steppic grasslands. We generated 18 variables from an ALS dataset collected in the growing (leaf-on) season. Elevation is a key factor determining the patterns of vegetation types in the landscape, and hence 3 additional variables were based on a digital terrain model (DTM) generated from an ALS dataset collected in the dormant (leaf-off) season. We classified the vegetation into 24 classes based on these 21 variables, at a pixel size of 1 m. Two groups of variables with and without the DTM-based variables were used in a Random Forest classifier, to estimate the influence of elevation, on the accuracy of the classification. The resulting classes at Level 4, based on associations, were aggregated at three levels — Level 3 (11 classes), Level 2 (8 classes) and Level 1 (5 classes) — based on species pool, site conditions and structure, and the accuracies were assessed. The classes were also aggregated based on Natura 2000 habitat types to assess the accuracy of the classification, and its usefulness for the monitoring of habitat quality. The vegetation could be classified into dry grasslands, wetlands, weeds, woody species and man-made features, at Level 1, with an accuracy of 0.79 (Cohen’s kappa coefficient, κ). The accuracies at Levels 2–4 and the classification based on the Natura 2000 habitat types were κ: 0.76, 0.61, 0.51 and 0.69, respectively. Levels 1 and 2 provide suitable information for nature conservationists and land managers, while Levels 3 and 4 are especially useful for ecologists, geologists and soil scientists as they provide high resolution data on species distribution, vegetation patterns, soil properties and on their correlations. Including the DTM-based variables increased the accuracy (κ) from 0.73 to 0.79 for Level 1. These findings show that the structural and spectral attributes of ALS echoes can be used for the classification of open landscapes, especially those where vegetation is influenced by elevation, such as coastal salt marshes, sand dunes, karst or alluvial areas; in these cases, ALS has a distinct advantage over other remotely sensed data. 相似文献
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二向反射分布函数包含地表反射的方向性特征信息。研究二向反射分布函数BRDF(Bidirectional Reflectance Distribution Function)形状对植被结构参数的敏感性,有助于理解植被的二向性反射规律,进而反演植被参数。本文耦合双冠层反射率模型和核驱动的罗斯厚核-李氏稀疏互易核模型,利用EFAST全局敏感性分析方法,以各向异性平整指数为BRDF形状变化的衡量指标,研究了不同天空光比例(SKYL)下,各向异性平整指数AFX对植被参数敏感度的变化,以及SKYL=0.1时AFX的敏感性。结果表明:(1)在红波段,上、下层叶面积指数、上层叶绿素含量,以及上层叶倾角分布是AFX的敏感参数,在近红外波段,上、下层叶面积指数LAI是AFX的敏感参数。(2)冠层尺度上的参数敏感度总体大于叶片尺度。(3)晴天时(SKYL=0.1),红波段主敏感度较大的参数分别是上层LAI、上层叶倾角分布和下层叶片结构参数,近红外波段主敏感度较大的参数主要是上、下层LAI。 相似文献
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基于倾斜摄影测量的三维建模已成为城市级实景三维建设的发展趋势。在城市三维模型中,由于植被表面不平整,需要用大量的三角面来刻画,但这并不是实景三维数据记录和表达的重点,并且大量的数据会给模型展示和应用带来很大的困难。因此,本文提出了一种顾及地物类别的倾斜摄影三维模型简化方法。首先,计算三维模型的纹理信息和几何信息,结合马尔可夫随机场(MRF)顾及空间一致性的优点提取植被;然后,采用二次误差测度(QEM)算法简化特定的植被区域;最后,对简化后的白膜模型进行纹理重映射。试验结果表明,本文方法能够准确提取并有效简化植被区域,纹理重映射的结果在外观上也与原始模型相差无几,取得了预期的良好效果。 相似文献
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利用Landsat ETM+分析城市热岛与下垫面的空间分布关系 总被引：3，自引：0，他引：3
采用数理统计与空间统计相结合的方法,利用Landsat ETM 数据对北京、上海、沈阳和武汉等4个大城市的夏季城市热岛相对强度与城市下垫面的空间分布关系进行对比研究。用混合像元线性光谱分解方法提取的城市植被覆盖度与不透水面表征城市下垫面;用城市地表亮温与水体亮温差值表征城市热岛相对强度。结果显示,4个城市的植被覆盖、不透水面与热岛强度的分布呈较强的空间正自相关,并且存在较为一致的自相关范围,该范围相当于城市街道与建筑组合特征尺度;自相关引起的结构性是导致3者空间分布异质性的主要因素。植被覆盖对城市热岛的缓解效果与不透水面对城市热岛的增强作用均呈分段线性特征,但区域差异较为明显;交叉相关系数曲线则显示出相关性的空间异质性与多尺度现象,同时存在一个约550 m的空间作用特征尺度。该研究结果有助于在城市规划实践中合理配置建筑与植被的间隔和比例,以缓解城市热岛效应。 相似文献
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基于TM的辐射传输模型反演叶面积指数可行性研究 总被引：4，自引：1，他引：4
基于PROSAIL辐射传输模型，引入土壤反射指数SRI来简化模型，提出直接从反射率计算SRI的方法； 同时，针对不同的植被状况，采取不同波段组合对模型的参数进行敏感性分析，确定自由参数与反演波段组合，提出一种基于不同植被状况的叶面积指数反演策略； 最后，应用遗传算法对模拟的TM光谱反射数据进行实验。结果表明，对于LAI＜3的植被，反演精度较高； 但是对于LAI＞3的植被，反演精度较低，其原因主要是冠层反射对LAI不再敏感。因此，辐射传输模型反演LAI有一定适用范围，只有在此范围内LAI的反演精度才可靠。 相似文献
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一种改进顶帽变换与LBP高程纹理的城区建筑物提取算法 总被引：1，自引：1，他引：0
利用LiDAR数据的建筑物提取存在植被点与建筑物点难以区分的问题,利用航空影像进行城区建筑物提取则无法有效剔除阴影区域植被。本文融合LiDAR和航空影像两种数据源,提出了改进顶帽变换及局部二进制模式(LBP)高程纹理分析的建筑物提取算法。首先将LiDAR数据进行规则格网化,通过改进顶帽变换提取地面数据点,然后根据航空影像计算归一化差值植被指数(NDVI)值进行植被粗提取,计算LBP高程纹理,精细区分植被点与建筑物点,最后利用形态学操作填充建筑物孔洞,以检测出的建筑物点为种子点进行区域生长,得到完整的建筑物点集合。试验基于ISPRS提供的Vaihingen数据集中复杂多植被城区场景,试验结果表明,本文算法能够有效区分植被与建筑物,实现建筑物准确提取。 相似文献
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基于SVM决策支持树的城市植被类型遥感分类研究 总被引：17，自引：0，他引：17
城市植被类型不同,生物量不同,其生态功能与绿化效应也不同。在目前难直接获取城市“绿量”实测数据的情况下,可以绿地面积和植被类型间接反映绿地的生物量和绿化效应。本文利用高分辨率卫星影像IKONOS,以实验区与验证区城市植被类型信息为对象,在对常用的参数和非参数分类方法进行对比实验的基础上,对SVM的核函数进行了分析,构建了基于SVM决策树的城市植被类型分类模型。分类实验结果表明：与其他传统方法分类结果比较,SVM的决策树分类方法对植被类型的分类精度达到83．5％,绿化面积总精度接近95％,取得了良好的效果。 相似文献
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《ISPRS Journal of Photogrammetry and Remote Sensing》1999,54(1):50-60
Conventional multispectral classification methods show poor performance with respect to detection of urban object classes, such as buildings, in high spatial resolution satellite images. This is because objects in urban areas are very complicated with respect to both their spectral and spatial characteristics. Multispectral classification detects object classes only according to the spectral information of the individual pixels, while a large amount of spatial information is neglected. In this study, a technique is described which attempts to detect urban buildings in two stages. The first stage is a conventional multispectral classification. In the second stage, the classification of buildings is improved by means of their spatial information through a modified co-occurrence matrix based filtering. The direction dependence of the co-occurrence matrix is utilised in the filtering process. The method has been tested by using TM and SPOT Pan merged data for the whole area of the city of Shanghai, China. After the co-occurrence matrix based filtering, the average user accuracy increased by about 46% and the average Kappa statistic by about 57%. This result is about 26% better than the accuracy improvement through normal texture filtering. The method presented in this study is very useful for a rapid estimation of urban building and city development, especially in metropolitan areas of developing countries. 相似文献