共查询到20条相似文献，搜索用时 31 毫秒
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基于支持向量机的航空影像纹理分类研究 总被引：8，自引：0，他引：8
提出一种用SVM解决航空影像纹理分类的方法。在利用一些常用的纹理特征的基础上，将SVM用于航空影像纹理分类，有效地解决了特征选择难和高维数问题。试验表明，这种方法可以取得较好的结果。 相似文献
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面向对象分类特征优化选取方法及其应用 总被引：3，自引：1，他引：2
与传统基于像元的分类方法比较,面向对象的分类方法可利用的地物信息更加丰富,然而如何从众多信息中筛选出能够有效提取不同地物的分类特征,从而提高分类效率与精度,是使用面向对象方法分类时急需解决的问题。SEaTH算法（分离阈值法）是一种有效的自动选取分类特征并计算阈值的方法,但其只考虑了类间距离,容易存在信息的冗余,从而对分类精度造成一定影响。本文在SEaTH算法的基础上,综合考虑了特征间的相关性、类间距离以及类内距离,对SEaTH算法进行了优化,并将改进前后的两种方法运用到广东省肇庆市TM影像及环境一号卫星影像土地覆盖分类中进行对比分析。实验结果表明,改进后的方法筛选出的特征在提取地物上更为有效,尤其使耕地的分类精度提高了12.26%,使分类总体精度由80%提高到了85.26%。改进后的方法对不易获取多时相影像的地区的土地覆盖分类具有重要意义。 相似文献
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Khalid Biro Biswajeet Pradhan Hussein Sulieman Manfred Buchroithner 《Journal of the Indian Society of Remote Sensing》2013,41(3):539-553
Recently, object-oriented classification techniques based on image segmentation approaches are being studied using high-resolution satellite images to extract various thematic information. In this study different types of land use/land cover (LULC) types were analysed by employing object-oriented classification approach to dual TerraSAR-X images (HH and HV polarisation) at African Sahel. For that purpose, multi-resolution segmentation (MRS) of the Definiens software was used for creating the image objects. Using the feature space optimisation (FSO) tool the attributes of the TerraSAR-X image were optimised in order to obtain the best separability among classes for the LULC mapping. The backscattering coefficients (BSC) for some classes were observed to be different for HH and HV polarisations. The best separation distance of the tested spectral, shape and textural features showed different variations among the discriminated LULC classes. An overall accuracy of 84 % with a kappa value 0.82 was resulted from the classification scheme, while accuracy differences among the classes were kept minimal. Finally, the results highlighted the importance of a combine use of TerraSAR-X data and object-oriented classification approaches as a useful source of information and technique for LULC analysis in the African Sahel drylands. 相似文献
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Cici Alexander Kevin Tansey Jörg Kaduk David Holland Nicholas J. Tate 《ISPRS Journal of Photogrammetry and Remote Sensing》2010,65(5):423-432
Airborne laser scanning (ALS) data are increasingly being used for land cover classification. The amplitudes of echoes from targets, available from full-waveform ALS data, have been found to be useful in the classification of land cover. However, the amplitude of an echo is dependent on various factors such as the range and incidence angle, which makes it difficult to develop a classification method which can be applied to full-waveform ALS data from different sites, scanning geometries and sensors. Additional information available from full-waveform ALS data, such as range and echo width, can be used for radiometric calibration, and to derive backscatter cross section. The backscatter cross section of a target is the physical cross sectional area of an idealised isotropic target, which has the same intensity as the selected target. The backscatter coefficient is the backscatter cross section per unit area. In this study, the amplitude, backscatter cross section and backscatter coefficient of echoes from ALS point cloud data collected from two different sites are analysed based on urban land cover classes. The application of decision tree classifiers developed using data from the first study area on the second demonstrates the advantage of using the backscatter coefficient in classification methods, along with spatial attributes. It is shown that the accuracy of classification of the second study area using the backscatter coefficient (kappa coefficient 0.89) is higher than those using the amplitude (kappa coefficient 0.67) or backscatter cross section (kappa coefficient 0.68). This attribute is especially useful for separating road and grass. 相似文献
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LiDAR回波强度数据的校正处理可以提升其在地物分类识别方面的利用价值。本文研究发现倾斜地物(如三角形、拱形房顶等)的自身倾角会改变激光反射角进而影响回波强度,而现有归一化校正模型缺乏对地物倾角的计算分析。随后结合LiDAR数据的采集方式和特点,以及倾斜地物的结构特征,提出了一种加入地物倾角计算的改进型归一化校正算法。首先,对激光点的回波强度值进行基于传输距离值的初步校正;然后,以平面距离值、高程、回波强度值差异为参考,判定激光点是否属于同一倾斜地物,若满足条件,则计算出激光点的趋势角,并依据制定的判定规则确定趋势角的正负;最后,将趋势角计算结果与扫描角数值结合计算出对应的反射角,并重新对回波强度值进行计算校正。实验表明,蓝铁皮材质的三角形、拱形房顶的回波强度值范围和均方差缩小,且采集的8种地物样本的回波强度均值的差异性与激光反射率测量结果基本一致,说明该算法能够有效识别具有一定面积且材质均一的倾斜人工地物并计算修正其自身倾角对回波强度带来的影响,从而进一步提高基于回波强度值的地物可分性。 相似文献
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Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of this paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy problems related to large ALS point spacing.Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline. 相似文献
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In this letter, a novel method of land-surface classification using surface-reflected global positioning system (GPS) signals in combination with digital imagery is presented. Two GPS-derived classification features are merged with visible image data to create terrain moisture classes, defined here as visibly identifiable terrain or landcover classes containing a surface/soil moisture component. As compared to using surface imagery alone, classification accuracy is significantly improved for a number of visible classes when adding GPS-based signal features. Since the strength of the reflected GPS signal is proportional to the amount of moisture in the surface, the use of these GPS features provides information about the surface that is not obtainable using visible wavelengths alone. Application areas include hydrology, precision agriculture, and wetlands mapping 相似文献
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Abstract A classification method was developed for mapping land cover in NE Costa Rica at a regional scale for spatial input to a biogeochemical model (CENTURY). To distinguish heterogeneous cover types, unsupervised classifications of Landsat Thematic Mapper data were combined with ancillary and derived data in an iterative process. Spectral classes corresponding to ground control types were segregated into a storage raster while ambiguous pixels were passed through a set of rules to the next stage of processing. Feature sets were used at each step to help sort spectral classes into land cover classes. The process enabled different feature sets to be used for different types while recognizing that spectral classification alone was not sufficient for separating cover types that were defined by heterogeneity. Spectral data included the TM reflective bands, principal components and the NDVI. Ancillary data included GIS coverages of swamp extents, banana plantation boundaries and river courses. Derived data included neighborhood variety and majority measures that captured texture. The final map depicts 18 land cover types and captures the general patterns found in the region. Some confusion still exists between closely related types such as pasture with different amounts of tree cover. 相似文献
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通过分离目标反射率、入射角及距离三个因素,利用Weierstrass定理,将激光强度表示成三个因素多项式函数的乘积形式,提出一种新的地面三维激光扫描（terrestrial laser scanning,TLS）激光强度改正模型。利用不同反射率的漫反射目标,设计入射角与距离实验,确定各个多项式的次数及系数,求取改正后激光强度值。实验结果表明,与现有改正模型进行比较,本文模型能精确地对激光强度值进行改正,并可以推广到自然目标强度数据的改正。 相似文献
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随着激光雷达技术的发展及广泛应用,点云数据的分类及理解成为了目前一个研究热点。本文研究了较复杂的电力线路走廊场景的点云自动分类方法,目标类别为地面、植被、建筑物、电力塔、电力线等。本文首先归纳、定义了点云分类所需的关键特征,并利用JointBoost实现地物分类;同时,考虑到点云数据量大,其分类速度较慢,本文结合地物空间上的相互关联关系,提出了一种序列化的点云分类及特征降维方法。该方法在保证分类精度的前提下,使分类所需特征维数降低,缩短了分类所需时间。实际的电力线路走廊的激光扫描点云数据分类实验证明本文研究的分类方法的有效性。 相似文献
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欧氏聚类算法是多元统计中的一种重要分类方法,可以将其应用于测绘领域中点云数据的分割。本文首先计算点云数据中两点之间的欧氏距离,将距离小于指定阈值作为分为一类的判定准则;然后迭代计算,直至所有的类间距大于指定阈值,完成欧氏聚类分割。具体步骤为：①利用Octree法建立点云数据拓扑组织结构;②对每个点进行k近邻搜索,计算该点与k个邻近点之间的欧氏距离,最小归为一类;③设置一定的阈值,对步骤②迭代计算,直至所有类与类之间的距离大于指定阈值。试验证明,欧氏聚类算法对不同测量技术手段获取的点云数据均具有适用性,可以成功对点云数据进行分割,分割效果良好。 相似文献
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Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data 总被引：2，自引：0，他引：2
Feature selection is a key task in remote sensing data processing, particularly in case of classification from hyperspectral images. A logistic regression (LR) model may be used to predict the probabilities of the classes on the basis of the input features, after ranking them according to their relative importance. In this letter, the LR model is applied for both the feature selection and the classification of remotely sensed images, where more informative soft classifications are produced naturally. The results indicate that, with fewer restrictive assumptions, the LR model is able to reduce the features substantially without any significant decrease in the classification accuracy of both the soft and hard classifications 相似文献
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The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008–2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland. 相似文献
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提出了一种基于对象的船载激光扫描数据内河船舶目标检测方法。该方法基于船舶目标的高程信息和几何特征,利用投影和图像形态学运算方法快速确定包含船舶目标的兴趣区域;然后针对船舶目标数据的空间分布特征,对兴趣区域点云数据进行欧式距离分割和优化处理,生成实体对象,并构建高差、外包围盒和局部密度等特征构成的向量,采用支持向量机进行二元分类,提取出船舶目标。该方法成功地检测出船载激光扫描数据中的船舶目标,船舶目标检测的准确率较高。 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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提出一种利用属类概率距离构图的半监督学习算法,并应用于高光谱图像分类。首先,该算法利用基于分类的稀疏表达方法来预估未标记样本的属类概率向量,然后,利用这个概率向量对描述数据相似性的距离函数进行改造,改造后的距离函数能有效扩大异类样本点之间的距离,在新的距离函数的度量下,每个样本点的邻域中可包含更多同类的样本点。最后,将该距离函数应用于半监督学习线性邻域传播算法和标签传播算法中。在Hyperion 和AVIRIS高光谱遥感图像上的实验结果表明：相比于传统的基于图的半监督学习算法,该算法能有效提高高光谱遥感图像分类精度。 相似文献
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针对高光谱影像分类面临的小样本问题,提出了一种深度少样例学习算法,该算法在训练过程中通过模拟小样本分类的情况来训练深度三维卷积神经网络提取特征,其提取得到的特征具有较小类内间距和较大的类间间距,更适合小样本分类问题,且能用于不同的高光谱数据,具有更好的泛化能力。利用训练好的模型提取目标数据集的特征,然后结合最近邻分类器和支持向量机分类器进行监督分类。利用Pavia大学、Indian Pines和Salinas 3组高光谱影像数据进行分类试验,试验结果表明,该算法能够在训练样本较少的情况下（每类地物仅选取5个标记样本作为训练样本）取得优于传统半监督分类方法的分类精度。 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