共查询到20条相似文献，搜索用时 31 毫秒
1.
Geoffrey J. Hay Guillermo Castilla Michael A. Wulder Jose R. Ruiz 《International Journal of Applied Earth Observation and Geoinformation》2005,7(4):339-359
Over the last decade the analysis of Earth observation data has evolved from what were predominantly per-pixel multispectral-based approaches, to the development and application of multiscale object-based methods. To empower users with these emerging object-based approaches, methods need to be intuitive, easy to use, require little user intervention, and provide results closely matching those generated by human interpreters. In an attempt to facilitate this, we present multiscale object-specific segmentation (MOSS) as an integrative object-based approach for automatically delineating image-objects (i.e., segments) at multiple scales from a high-spatial resolution remotely sensed forest scene. We further illustrate that these segments cognitively correspond to individual tree crowns, ranging up to forest stands, and describe how such a tool may be used in computer-assisted forest inventory mapping. MOSS is composed of three primary components: object-specific analysis (OSA), object-specific upscaling (OSU), and a new segmentation algorithm referred to as size constrained region merging (SCRM). The rationale for integrating these methods is that the first two provide the third with object-size parameters that otherwise would need to be specified by a user. Analysis is performed on an IKONOS-2 panchromatic image that represents a highly fragmented forested landscape in the Sooke Watershed on southern Vancouver Island, BC, Canada. 相似文献
2.
3.
多尺度分割的高分辨率遥感影像变化检测 总被引：4，自引：1，他引：3
针对高空间分辨率的遥感影像,提出了一种基于多尺度分割的变化检测算法。采用Mean-Shift分割算法对影像进行多尺度分割,构建了不同尺度上的地理对象,以不同尺度上的地理对象灰度均值构建了变化检测的多尺度特征向量,采用变化矢量分析法获得最后的变化检测结果。以城镇区和农田区的Quick Bird影像对本文算法进行了检验,从精度评价的效果来看,无论城镇区还是农田区,采用面向对象的变化检测方法精度都高于基于单像素的检测方法,且当尺度层数固定时,多尺度组合的变化检测结果优于单一尺度的变化检测结果,对城镇、农田区域的变化检测的精度分别达到87.57%和81.55%。本文算法既可以顾及大面积同质区域变化,又可以反映小的地物目标及边缘部分的变化,能够很好地满足城镇、农田等不同环境背景下的变化检测需求,在国土资源监测中具有一定的应用价值。 相似文献
4.
A Multiscale Approach for Spatio-Temporal Outlier Detection 总被引：1，自引：0，他引：1
A spatial outlier is a spatially referenced object whose thematic attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. It represents an object that is significantly different from its neighbourhoods even though it may not be significantly different from the entire population. Here we extend this concept to the spatio‐temporal domain and define a spatial‐temporal outlier (ST‐outlier) to be a spatial‐temporal object whose thematic attribute values are significantly different from those of other spatially and temporally referenced objects in its spatial or/and temporal neighbourhoods. Identification of ST‐outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability or deformation. Many methods have been recently proposed to detect spatial outliers, but how to detect the temporal outliers or spatial‐temporal outliers has been seldom discussed. In this paper we propose a multiscale approach to detect ST‐outliers by evaluating the change between consecutive spatial and temporal scales. A four‐step procedure consisting of classification, aggregation, comparison and verification is put forward to address the semantic and dynamic properties of geographic phenomena for ST‐outlier detection. The effectiveness of the approach is illustrated by a practical coastal geomorphic study. 相似文献
5.
A comparison of three image-object methods for the multiscale analysis of landscape structure 总被引：4，自引：0，他引：4
Geoffrey J. Hay Thomas Blaschke Danielle J. Marceau Andr Bouchard 《ISPRS Journal of Photogrammetry and Remote Sensing》2003,57(5-6):327
Within the conceptual framework of Complex Systems, we discuss the importance and challenges in extracting and linking multiscale objects from high-resolution remote sensing imagery to improve the monitoring, modeling and management of complex landscapes. In particular, we emphasize that remote sensing data are a particular case of the modifiable areal unit problem (MAUP) and describe how image-objects provide a way to reduce this problem. We then hypothesize that multiscale analysis should be guided by the intrinsic scale of the dominant landscape objects composing a scene and describe three different multiscale image-processing techniques with the potential to achieve this. Each of these techniques, i.e., Fractal Net Evolution Approach (FNEA), Linear Scale-Space and Blob-Feature Detection (SS), and Multiscale Object-Specific Analysis (MOSA), facilitates the multiscale pattern analysis, exploration and hierarchical linking of image-objects based on methods that derive spatially explicit multiscale contextual information from a single resolution of remote sensing imagery. We then outline the weaknesses and strengths of each technique and provide strategies for their improvement. 相似文献
6.
地理要素的时空变化分析对于了解和掌握地表的规律性有着重要的作用。利用地面站点观测、实地调查等传统方式获取数据,对地理要素进行时空变化分析是最常用的方法。但该类方法往往表现的是"点尺度"观测,不能在大尺度情况下准确地反映地表的时空变化信息。遥感卫星能以一定的时间间隔获得空间连续的对地"面尺度"观测数据,然而其特定的空间和时间分辨率使其获取的地表信息仍十分有限。同时地表的空间异质性和个别地表短时间内的快速变化,使得利用遥感数据对地理要素进行时空变化分析时,时空变化分析结果会随观测尺度而发生改变。本文从空间尺度和时间尺度两方面综述遥感数据时空尺度对地理要素的时空变化分析产生的影响和原因,并针对这些问题总结了减小时空尺度对结果不确定性影响的现行方法。可以通过多源遥感协同观测和反演、尺度转换、空间建模等方法减小空间尺度引起的不确定性;通过联合多时相遥感数据的方法减小时间尺度引起的不确定性。在实际应用中,应根据所观测地理要素的实际情况,综合分析选择合适的方法。 相似文献
7.
按照２ＤＧａｂｏｒ函数的特点和视觉机制，提出了用来捕捉纹理基元的纹理检测器函数，然后基于纹理检测器和新近发展的小波多尺度分解理论，提出了影象纹理的多尺度分析方法，并按照神经动力学的侧抑制和端点抑制等理论，建立了对多尺度纹理特征的融合方法。这一多尺度纹理分析方法在介于空间域和付里叶域之间，为纹理分析提供了一个层次性的分析框架，能够检测不同尺度的纹理信息并与视觉纹理处理机制相一致，有效地提高了纹理的识别能力。 相似文献
8.
India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion. 相似文献
9.
10.
A map‐algebra‐based method for automatic change detection and spatial data updating across multiple scales 
下载免费PDF全文
下载免费PDF全文 The availability of geospatial data has increased significantly over recent decades. As a result, the question of how to update spatial data across different scales has become an attractive topic. One promising strategy is to use an updated larger‐scale dataset as a reference for detecting and updating changed objects represented in a to‐be‐updated smaller‐scale dataset. For such an update method, an understanding of the different types of changes that can occur is crucial. Using polygonal building data as an example, this study examines the various possible changes from different perspectives, such as the reasons for their occurrence, the forms in which they manifest, and their effects on output. Then, we apply map algebra theory to establish a cartographic model for updating polygonal building data. Supported by concepts of map algebra, an update procedure involving change detection, filtering, and fusion is implemented through a series of set operations. In addition to traditional polygon overlay functions, the constrained Delaunay triangulation model and knowledge of map generalization procedures are employed to construct set operations. The proposed method has been validated through tests using real‐world data. The experimental results show that our method is effective for updating 1:10k map data using 1:2k map data. 相似文献
11.
High-spatial resolution remote sensing imagery provides unique opportunities for detailed characterization and monitoring of landscape dynamics. To better handle such data sets, change detection using the object-based paradigm, i.e., object-based change detection (OBCD), have demonstrated improved performances over the classic pixel-based paradigm. However, image registration remains a critical pre-process, with new challenges arising, because objects in OBCD are of various sizes and shapes. In this study, we quantified the effects of misregistration on OBCD using high-spatial resolution SPOT 5 imagery (5 m) for three types of landscapes dominated by urban, suburban and rural features, representing diverse geographic objects. The experiments were conducted in four steps: (i) Images were purposely shifted to simulate the misregistration effect. (ii) Image differencing change detection was employed to generate difference images with all the image-objects projected to a feature space consisting of both spectral and texture variables. (iii) The changes were extracted using the Mahalanobis distance and a change ratio. (iv) The results were compared to the ‘real’ changes from the image pairs that contained no purposely introduced registration error. A pixel-based change detection method using similar steps was also developed for comparisons. Results indicate that misregistration had a relatively low impact on object size and shape for most areas. When the landscape is comprised of small mean object sizes (e.g., in urban and suburban areas), the mean size of ‘change’ objects was smaller than the mean of all objects and their size discrepancy became larger with the decrease in object size. Compared to the results using the pixel-based paradigm, OBCD was less sensitive to the misregistration effect, and the sensitivity further decreased with an increase in local mean object size. However, high-spatial resolution images typically have higher spectral variability within neighboring pixels than the relatively low resolution datasets. As a result, accurate image registration remains crucial to change detection even if an object-based approach is used. 相似文献
12.
利用GNSS数据,基于球面小波构建了GNSS多尺度应变场的估计模型,给出了球面小波位置和尺度的选取、模型正则化因子和参数的估计方法。为检验所构建模型的正确性,根据负位错理论正演了震间闭锁断层区域地表速度场作为模拟数据源,估计并分析了闭锁状态下走滑断层区域应变场分布特征,结果与实际吻合。模拟不同空间影响范围的闭锁逆冲断层形变,开展了多尺度应变场检测不同空间尺度形变信息的试验。结果表明,对于50km影响范围的小断层形变信号,在大尺度(第4—7尺度)应变场中并没有体现。而在小尺度(第8尺度)应变场中表现得非常明显。对于150km影响范围的大断层形变,在小尺度(第8尺度)应变场中仅出现了一小部分信息,而在大尺度(第4—7尺度)应变场中表现得更加完整和明显。由此得出,不同空间影响范围的地壳形变信息会在相应尺度的应变场中得以体现;小尺度应变场具有检测局部地壳形变信息的优势。 相似文献
13.
利用多尺度融合进行面向对象的遥感影像变化检测 总被引：1，自引：0，他引：1
在面向对象的变化检测过程中,确定对象的最优分割尺度直接关系到后续的变化信息提取与分析。针对该问题,提出了基于多尺度分割与融合的对象级变化检测新方法。首先,利用由细到粗的尺度分割来获取不同尺寸的目标对象,然后依据对象的特征进行变化向量分析得到各个尺度上的变化检测结果。为了提高变化检测的精度,本文引入模糊融合及两种决策级融合方法进行多尺度融合,并利用SPOT5多光谱遥感图像进行试验。与像素级的变化检测方法相比,总体精度提高了10%左右,试验结果证明了这几种融合策略的有效性和可行性。 相似文献
14.
《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
15.
针对当前高分辨率遥感影像多层次分割尺度参数设置缺少理论框架支持、人为因素影响较多等缺点,提出一种引入松弛因子的高分辨率遥感影像自动多层次分割方法。该方法利用1个松弛因子调节引导区域对象合并的异质性值大小,通过控制每次递归合并区域的对象个数,提高了整体分割的速度;以区域对象间异质性平均值作为基数,引入另一个松弛因子控制分割过程中层次输出的尺度参数,使整个分割过程自动得到不同尺度的多层次分割结果。实验结果表明,该方法具有较高的分割质量,能够满足遥感影像分析及地物提取的精度要求,并且减少了人为因素影响,提高了自动化程度。但是,对于复杂图像内容的地物目标边界处理和减少狭长区域对象的出现还需要进一步深入研究和实践。 相似文献
16.
跨比例尺新旧居民地目标变化分析与决策树识别 总被引：1，自引：1，他引：0
变化分析与探测是跨比例尺地图数据更新的核心问题之一。以往研究主要关注时间维上地理实体时空演化引起的地图目标变化,甚至将地图目标变化等同于地理实体真实变化,忽略了尺度维上由地图综合导致的表达变化。本文以居民地数据为例,从表层形式和深层缘由对跨比例尺新旧地图数据间的目标变化进行深入分析。在此基础上,引入机器学习领域的决策树方法构建变化信息识别模型。该模型的目标是判别时态变化和表达变化两种类型,从而提取用于更新小比例尺地图数据的真正变化信息。结合广州市多比例尺地图数据库更新任务及实际数据进行验证,结果显示设计的变化探测模型可以达到90%以上的整体精度。 相似文献
17.
面向对象的多特征分级CVA遥感影像变化检测 总被引：1，自引：0，他引：1
变化矢量分析CVA方法在中低分辨率遥感影像变化检测中已得到广泛应用,但由于高分辨率遥感影像存在不同地物尺度差异大、不同类别地物光谱相互重叠的问题,因此对于高分影像的变化检测具有局限性。为提高高分影像变化检测精度,提出了一种面向对象的多特征分级CVA变化检测方法,首先,利用基于区域邻接图的影像分割方法分别对两时相遥感影像进行多尺度分割,提取分割图斑的光谱、纹理和形状特征;然后,在各级尺度下,分别运用随机森林方法进行特征选择,计算CVA变化强度图;最后,根据信息熵对多级变化强度图进行自适应融合,利用Otsu阈值法检测变化区域,并与仅考虑光谱特征的分级CVA变化检测方法、像元级多特征CVA变化检测方法以及仅考虑光谱特征的像元级CVA变化检测方法进行比较分析。实验表明:与比较方法相比,本文方法的变化检测精度较高,误检率和漏检率较低。 相似文献
18.
19.
基于多尺度特征融合和支持向量机的高分辨率遥感影像分类 总被引：10，自引：1，他引：10
相对传统的中低分辨率遥感数据而言,高空间分辨率遥感影像同一地物内部丰富的细节得到表征,空间信息更加丰富,地物的尺寸、形状以及相邻地物的关系得到更好的反映,但其光谱统计特性不如中低分辨率影像稳定,类内光谱差异较大,而传统分类方法仅依据像元的光谱值,因此在高分辨率影像分类中,传统方法往往不能获得好的结果。在此背景下,提出了一种多尺度空间特征融合的分类方法,旨在利用不同尺度的空间邻域特征弥补传统方法的不足。其基本过程是：首先针对不同尺度特点,用小波变换压缩空间邻域特征,并结合支持向量机得到不同尺度下的分类结果,然后根据尺度选择因子为每个像元选择最佳的类别。文中QuickBird和IKONOS影像实验证明该算法能有效提高高分辨率影像解译的精度。 相似文献
20.
Data refinement refers to the processes by which a dataset’s resolution, in particular, the spatial one, is refined, and is thus synonymous to spatial downscaling. Spatial resolution indicates measurement scale and can be seen as an index for regular data support. As a type of change of scale, data refinement is useful for many scenarios where spatial scales of existing data, desired analyses, or specific applications need to be made commensurate and refined. As spatial data are related to certain data support, they can be conceived of as support-specific realizations of random fields, suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finer-resolution ones. In this paper, geostatistical methods for downscaling are described, and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China, with the latter’s majority grid cells used as surrogate reference data. It was found that proper structural modeling is important for achieving increased accuracy in data refinement; here, structural modeling can be done through proper decomposition of elevation fields into trends and residuals and thereafter. It was confirmed that effects of semantic differences on data refinement can be reduced through properly estimating and incorporating biases in local means. 相似文献