共查询到20条相似文献，搜索用时 15 毫秒
1.
With the development of Volunteered Geographical Information (VGI) data, the OpenStreetMap has high research value in terms of project activity, social influence, urban development, application scope, and historical richness and the number of buildings or roads is increasing every day. However, how to evaluate the quality of a large amount OpenStreetMaps efficiently and accurately is still not fully understood. This article presents the development of an approach regarding multilevel stratified spatial sampling based on slope knowledge and official 1:1000 thematic maps as the reference dataset for OpenStreetMap data quality inspection of Hong Kong. This multilevel stratified spatial sampling plan is as follows: (1) The terrain characteristics of Hong Kong are fully considered by dividing grids into quality estimate strata based on the slope information; (2) Spatial sampling for the selection of grids or objects is used; (3) A more reliable sampling subset is made, regarding the representation of the entire OpenStreetMap dataset of Hong Kong. This sampling plan displays a 10% higher sampling accuracy, but without increasing the sample size, particularly as regards building completeness inspection compared with simple random sampling and systematic random sampling. This research promotes further applications of the Open-Street-Map dataset, thus enabling us to have a better understanding of the OpenStreetMap data quality in urban areas. 相似文献
2.
基于不同抽样方法的遥感面积测量方法研究 总被引：11，自引：0，他引：11
众多研究结果表明,遥感和抽样技术相结合可以有效地进行地物面积的测量。目前,随机抽样、系统抽样和分层抽样方式在
遥感抽样调查技术领域应用比较广泛。本文以遥感图像为基础,从不同角度对随机抽样、系统抽样及分层抽样（包括等样本量、等
面积、等丰度抽样）进行了有益探讨,分析发现： 对于同一地物,从平均误差百分比、标准差和极差3个角度分析,随机和系统抽
样反推得到的总量精度都低于分层抽样精度; 对于不同的地物类型,利用3种分层抽样方法反推的结果与地物所占百分比成正相关
,地物所占的百分比越大,反推的结果越好; 等样本量、等面积、等丰度分层抽样从平均误差百分比、标准差和极差3个角度分析
各有优势,跟地物所占的百分比也有密切关系。 相似文献
3.
Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse-resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogeneous urban environments. To address this problem, we derived endmember signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmember signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e. fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree and Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available. 相似文献
4.
人口抽样调查是通过人口样本估算区域人口总体的一种手段。由于人口分布通常具有空间差异性,传统的抽样调查理论难以满足日益增长的空间抽样需求,合理高效的人口空间抽样调查方法对于人口统计、研究人类活动、解决城市问题等有重要意义。本文提出一种基于多源信息与深度学习特征提取的人口空间抽样方法。在不透水面信息的辅助下,利用四叉树分割进行分层抽样,初步选择出可能存在人口分布的调查样本,并通过深度学习的常用模型——卷积神经网络估算样本建筑物密度,以辅助最终调查样本的选择与调查方案的制定。研究结果证明,该方法能够有效地筛选与人口分布密切相关的抽样区域,排除大量的无用样本,提高了人口调查的效率,节约了大量调查成本。 相似文献
5.
针对城市行道树的学习多分类问题，本文在综合分析城市行道树多分类特征的基础上，提出一种融合特征自动选取模型的自适应深度学习方法。基于随机森林法，学习行道树的特征重要性，通过特征消除方法舍弃不重要的特征，实现城市行道树多分类特征自动选取；在城市行道树分类特征工程提取的基础上，构建了城市行道树多分类问题的自适应深度学习方法，并采用交叉验证与参数搜索方法，对所提出的深度学习模型进行改进。试验结果表明，本文所提出的融合特征自动选取模型的自适应深度学习方法具有良好性能，解决了城市行道树多分类预测的准确性与泛化问题。 相似文献
6.
Supervised classification of quad-polarimetric SAR images is often constrained by the availability of reliable training samples. Active learning (AL) provides a unique capability at selecting samples with high representation quality and low redundancy. The most important part of AL is the criterion for selecting the most informative candidates (pixels) by ranking. In this paper, class supports based on the posterior probability function are approximated by ensemble learning and majority voting. This approximation is statistically meaningful when a large enough classifier ensemble is exploited. In this work, we propose to use extreme learning machines and apply AL to quad-polarimetric SAR image classification. Extreme learning machines are ideal because of their fast operation, straightforward solution and strong generalization. As inputs to the so-called active extreme learning machines, both polarimetric and spatial features (morphological profiles) are considered. In order to validate the proposed method, results and performance are compared with random sampling and state-of-the-art AL methods, such as margin sampling, normalized entropy query-by-bagging and multiclass level uncertainty. Experimental results for four quad-polarimetric SAR images collected by RADARSAT-2, AirSAR and EMISAR indicate that the proposed method achieves promising results in different scenarios. Moreover, the proposed method is faster than existing techniques in both the learning and the classification phases. 相似文献
7.
This paper presents a generative statistical approach to automatic 3D building roof reconstruction from airborne laser scanning point clouds. In previous works, bottom-up methods, e.g., points clustering, plane detection, and contour extraction, are widely used. Due to the data artefacts caused by tree clutter, reflection from windows, water features, etc., the bottom-up reconstruction in urban areas may suffer from a number of incomplete or irregular roof parts. Manually given geometric constraints are usually needed to ensure plausible results. In this work we propose an automatic process with emphasis on top-down approaches. The input point cloud is firstly pre-segmented into subzones containing a limited number of buildings to reduce the computational complexity for large urban scenes. For the building extraction and reconstruction in the subzones we propose a pure top-down statistical scheme, in which the bottom-up efforts or additional data like building footprints are no more required. Based on a predefined primitive library we conduct a generative modeling to reconstruct roof models that fit the data. Primitives are assembled into an entire roof with given rules of combination and merging. Overlaps of primitives are allowed in the assembly. The selection of roof primitives, as well as the sampling of their parameters, is driven by a variant of Markov Chain Monte Carlo technique with specified jump mechanism. Experiments are performed on data-sets of different building types (from simple houses, high-rise buildings to combined building groups) and resolutions. The results show robustness despite the data artefacts mentioned above and plausibility in reconstruction. 相似文献
8.
P S Roy S C Moharana S N Prasad I J Singh 《Journal of the Indian Society of Remote Sensing》1992,20(4):223-235
Changes brought in habitat conditions due to increasing human influences on natural areas have posed serious threat to wildlife. Remote Sensing has probably omerged as one of the most viable techniques to assess and monitor habitat conditions. Comparative analysis of maps of two-time period can provide authentic data with respect to changes brought in the habitat conditions. Chandaka Wildlife Sanctuary, covering an area of 213.71 sq. km in Orissa is one of the natural reserves of elephants which has undergone serious changes brought in through anthropogenic activities of urban areas of Cuttack and Bhubaneshwar lying within the proximity of the sanctuary. The natural reserve, an ideal habitat for elephants, was connected to neighbouring extensive forest belts. These connections have been either degraded or deforested over the years. The present study analyses the types of habitat available in the sanctuary using remote sensing data (aerial and satellite). Vegetation-type maps of 1975 have been prepared from B/W aerial photographs of 1:25,000 scale. For assessing the current vegetation types, maps have been prepared from Indian Remote Sensing Satellite (LISS II) false colour composite on 1:50,000 scale. Comparative evaluation of the maps indicates changes in the vegetation pattern, increase in mining and agriculture areas within the sanctuary. Stratified field sampling of vegetation types provide structural characteristics of the vegetation. Bamboo has been found to extend in the valleys and side slopes of the sanctuary area during past 15 years. An analysis on response of vegetation in all major vegetation types mapped have been made in the context of the invasion of Eupatorium odoratum. Finally, bamboo biomass has been assessed through stratified random sampling as it constitutes a major elephant food source. 相似文献
9.
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
10.
11.
Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type. 相似文献
12.
Four binary thematic maps with combinations of two spatial autocorrelation levels and two different class proportions are simulated to study their effect on the precision of accuracy measures from different sampling designs. A series of eleven sample sizes (from a minimum of 25 to a maximum of 1296) are simulated using three popular sampling designs, including simple random sampling (SRS), systematic sampling (SYS), and stratified random sampling (StrRS) on the four simulated maps. The conventional error matrix and related accuracy measures are calculated for each simulation, and the precision of different estimates of accuracy measures is compared among the three sampling designs.The selection of a particular sampling design and sample size depends on the spatial autocorrelation level, the class proportion difference, and the accuracy indices that a given application requires. In general, the class proportion difference has a greater impact on the performance of different sampling methods than the spatial autocorrelation level does on a map. For estimating the accuracy of individual classes, stratified sampling achieves better precision than SRS and SYS with smaller sample sizes, especially for estimating the small class. For estimating the overall accuracy, different sampling designs achieve very similar levels of precision with fewer samples. To achieve a better estimate of the kappa coefficient, stratified random sampling is recommended for use on a map with a high class proportion difference, while random sampling is preferred for a map with low spatial autocorrelation and a low class proportion difference. 相似文献
13.
Belkacem Marir Mahdi Kalla Fouzi Douak Abdelhamid Daamouche 《Journal of the Indian Society of Remote Sensing》2018,46(4):515-529
In this work, we present a new strategy of active learning, based on a modular version of support vector machine (MSVM) applied to urban remote sensing images in Algeria. In general, the training set is highly imbalanced, which gives more complex models; this difficulty is solved by dividing the problem at hand into a set of sub-problems, where each sub-model could be simpler to solve. The support vector machine is introduced to solve the problem of classification based on image remote sensing data related to atmospheric conditions and illumination reflectance. The aim of the proposed method is to improve the accuracy in order to understand the correlated elements of urban structures (the site, the built, the parcels, the network, the space), to generate the final classification result. In particular, we propose a new method based on the modular support vector machine (MSVM) adopted to active learning method, using three different clustering methods (i) k-means, (ii) fuzzy c-means (FCM), and (iii) Gustafson–Kessel (GKclust). Experimental results obtained on two QuickBird multispectral images of Sétif and Batna cities in the eastern of Algeria confirm the capabilities of the proposed methods based on the ensemble of model trained with different task decomposition compared to a traditional model using active learning. This method improves each class presents a main register in urban structure tissues. 相似文献
14.
Vegetation maps are essential tools for the conservation and management of landscapes as they contain essential information for informing conservation decisions. Traditionally, maps have been created using field-based approaches which, due to limitations in costs and time, restrict the size of the area for which they can be created and frequency at which they can be updated. With the increasing availability of satellite sensors providing multi-spectral imagery with high temporal frequency, new methods for efficient and accurate vegetation mapping have been developed. The objective of this study was to investigate to what extent multi-seasonal Sentinel-2 imagery can assist in mapping complex compositional classifications at fine spatial scales. We deliberately chose a challenging case study, namely a visually and structurally homogenous scrub vegetation (known as kwongan) of Western Australia. The classification scheme consists of 24 target classes and a random 60/40 split was used for model building and validation. We compared several multi-temporal (seasonal) feature sets, consisting of numerous combinations of spectral bands, vegetation indices as well as principal component and tasselled cap transformations, as input to four machine learning classifiers (Support Vector Machines; SVM, Nearest Neighbour; NN, Random Forests; RF, and Classification Trees; CT) to separate target classes. The results show that a multi-temporal feature set combining autumn and spring images sufficiently captured the phenological differences between the classes and produced the best results, with SVM (74%) and NN (72%) classifiers returning statistically superior results compared to RF (65%) and CT (50%). The SWIR spectral bands captured during spring, the greenness indices captured during spring and the tasselled cap transformations derived from the autumn image emerged as most informative, which suggests that ecological factors (e.g. shared species, patch dynamics) occurring at a sub-pixel level likely had the biggest impact on class confusion. However, despite these challenges, the results are auspicious and suggest that seasonal Sentinel-2 imagery has the potential to predict compositional vegetation classes with high accuracy. Further work is needed to determine whether these results are replicable in other vegetation types and regions. 相似文献
15.
Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas. 相似文献
16.
地图投影是地图学的重要研究内容。任何地图投影都不可避免地存在变形问题。针对地图投影的变形,本文提出球面大圆弧和互补比率均值相结合的地图投影面积变形与形状变形指标。通过算例验证和相关性分析,大圆指标一方面简化了小圆指标(即互补比率均值)的计算过程,并能与小圆指标的结果保持一致;另一方面,大圆指标与微分指标之间也具有较高的一致性(形状变形指标的皮尔森积矩相关系数大于0.988)。由于大圆指标不依赖于微分计算,且计算简捷,因此大圆指标更具通用性。本文进一步采用回归分析对大圆指标进行分析,结果表明,大圆指标与微分指标具有较好的线性关系(线性回归的平均误差小于1.10‰)。为了降低采样点数量和解决采样点不统一问题,本文还提出了基于随机采样的指标计算方法,并对随机方法进行了验证和分析。依据大圆指标与微分指标的一致性和线性关系,可以认为使用大圆指标能够有效地评估地图投影的变形情况。 相似文献
17.
Abstract Riparian vegetation has a fundamental influence on the biological, chemical and physical nature of rivers. The quantification of riparian landcover is now recognised as being essential to the holistic study of the ecosystem characteristics of rivers. Medium resolution satellite imagery is now commonly used as an efficient and cost effective method for mapping vegetation cover; however such data often lack the resolution to provide accurate information about vegetation cover within riparian corridors. To assess this, we measure the accuracy of SPOT multispectral satellite imagery for classification of riparian vegetation along the Taieri River in New Zealand. In this paper, we discuss different sampling strategies for the classification of riparian zones. We conclude that SPOT multispectral imagery requires considerable interpretative analysis before being adequate to produce sufficiently detailed maps of riparian vegetation required for use in stream ecological research. 相似文献
18.
S. P. S. Kushwaha I. J. Singh Subrato Paul 《Journal of the Indian Society of Remote Sensing》2003,31(4):283-290
Remote sensing is being increasingly used for forest resource inventory as it saves time and the cost. Aerial photographs
and satellite images have been effectively utilized for forest inventory all over the world. This study highlights the application
of IRS LISS-III imagery for inventorying the stand volume in Lachchhiwala Forest Range of Siwaliks. The satellite image was
visually interpreted for forest type and density stratification. Both random as well as stratified random sampling techniques
were used to see their impact on the volume estimates. Field sampling was done in the plots of 0.1 ha size. The total growing
stock in all types of forests in the study area was estimated to be 1.87 mill.m3, of which Sal Forest accounted for 1.32 mill.m3, Sal Mixed Forest for 0.09 mill.m3, Mixed Sal Forest for 0.08 mill.m3, Miscellaneous Forest for 0.06 mill.m3 and Forest Plantations for 0.02 mill.m3. The results were compared with an independent field-based inventory carried out by forest department. The two sampling methods
were compared by ratioing of the mean of variance (gain in precision) and it was found that the timber volume estimates using
stratified random sampling technique were 15 per cent more accurate than simple random sampling. The satellite image-based
inventory using stratified random sampling was found to have about 90 per cent correspondence with the inventory done by the
Forest Department. 相似文献
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20.
联合稳健跨域映射和渐进语义基准修正的零样本遥感影像场景分类 总被引：1，自引：0，他引：1
零样本影像分类技术旨在通过学习数据集的部分类别（可见类）,获得识别在训练阶段未出现类别（不可见类）的能力。该技术在遥感大数据时代具有重要现实意义。目前,遥感领域的零样本场景分类方法对于映射后的语义空间优化关注很少,导致已有方法的整体分类性能较差。基于这一考虑,本文提出了一种基于稳健跨域映射和渐进语义基准修正的零样本遥感影像场景分类方法。在训练的有监督学习模块,基于可见类的类别语义向量和场景影像样本,实现深度特征提取器学习和视觉空间到语义空间的稳健映射。在训练的无监督学习阶段,基于全体类别的类别语义向量和不可见类遥感影像样本,分别通过协同表示学习和k近邻算法来渐进修正不可见类类别的语义向量,从而缓解可见类语义空间与不可见类语义空间的漂移问题和自编码跨域映射模型映射后不可见类语义空间与协同表示后不可见类语义空间的偏移问题。在测试阶段,基于学习所得的深度特征提取器、自编码跨域映射模型和修正后的不见类语义向量,实现对不可见类遥感影像场景的分类。本文整合多个已有公开的遥感影像场景数据集,组建了一个新的遥感影像场景数据集,在此数据集上进行试验。试验结果表明本文提出的算法在多种不同的可见类与不可见类的划分情况下都明显优于已有公开零样本分类方法。 相似文献