Landscape spatial pattern is dependent not only on interacting physiographic and physiological processes, but also on the temporal and spatial scales at which the resulting patterns are assessed. To detect significant spatial changes occurring through space and time three fundamental components are required. First, a multiscale dataset must be generated. Second, a change detection framework must be applied to the multiscale dataset. Third, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce an object-specific multiscale digital change detection approach. This approach incorporates multitemporal SPOT Panchromatic (Pan) data, object-specific analysis (OSA), object-specific up-scaling (OSU), marker-controlled watershed segmentation (MCS) and image differencing change detection. By applying this framework to SPOT Pan data, image-objects that have changed between registration dates can be identified and delineated at their characteristic scale of expression. Results illustrate that this approach has the ability to automatically detect changes at multiple scales as well as suppress sensor related noise. This study was conducted in the forest region of the rebro Administrative Province, Sweden.

http://www.tandfonline.com/doi/abs/10.1080/01431160600617194

Thanks to such second- and third-generation sensor systems as Thematic Mapper, SPOT, and AVHRR, a user of digital satellite imagery for remote sensing of the earth's surface now has a choice of image scales ranging from 10 m to 1 km. The choice of an appropriate scale, or spatial resolution, for a particular application depends on several factors. These include the information desired about the ground scene, the analysis methods to be used to extract the information, and the spatial structure of the scene itself. A graph showing how the local variance of a digital image for a scene changes as the resolution-cell size changes can help in selecting an appropriate image scale. Such graphs are obtained by imaging the scene at fine resolution and then collapsing the image to successively coarser resolutions while calculating a measure of local variance. The local variance/resolution graphs for the forested, agricultural, and urban/suburban environments examined in this paper reveal the spatial structure of each type of scene, which is a function of the sizes and spatial relationships of the objects the scene contains. At the spatial resolutions of SPOT and Thematic Mapper imagery, local image variance is relatively high for forested and urban/suburban environments, suggesting that information-extracting techniques utilizing texture, context, and mixture modeling are appropriate for these sensor systems. In agricultural environments, local variance is low, and the more traditional classifiers are appropriate.

本文从面向对象的遥感信息提取中的尺度效应研究入手,对影像对象的分形维数、紧凑度、面积、 均值、标准差和与邻域均值差分等特征进行了实验。在此基础上,根据＂类内同质性大,类间异质性大＂的最佳分类原则,提出了面向对象的RMAS方法,该方法 的思想是,当对象RMAS值最大时,对象内部的异质性最小,对象外部的异质性最大,此时的分割尺度为类别提取的最优分割尺度。根据最优尺度下信息提取精度 最高的原理,实验验证了该方法的可行性,且能获得较好的分类结果。

<p>遥感数据的分辨率越来越高, 给地物信息提取提出了新的挑战。利用基于像元的分类技术和基于多尺度分割的面向对象分类技术对高分辨率影像进行分类实验, 分析地物大小、对象尺度与影像分辨率的关系。实验结果表明不同地物由于其空间尺度不同, 与之相适宜的空间分辨率和对象尺度也不同, 在适宜分辨率的影像提取有较高的精度, 在适宜的对象尺度上提取对象信息有更高的精度。分析也表明面向对象的多尺度影像分类技术适应了不同地物有其相适宜的空间分辨率, 在适宜尺度影像层中提取地物, 其分类精度大大高于基于像元的分类方法。</p>

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。

随着高分辨率遥感影像在矿山分类工作中的广泛应用,面向对象的多尺度分割技术成为研究热点。最优尺度的选取是此类技术的关键。为获取高分遥感影像最适宜的分割尺度,本文以赣州稀土矿山高分辨率遥感影像为源数据,选择与邻域绝对均值差分方差比方法 RMAS(Ratio of Mean Difference to Neighbors(ABS)to Standard Deviation)方法获取最优分割尺度。通过分割质量值对分割结果进行评价分析,研究验证了RMAS方法的可行性。

This paper addresses two challenging issues in unsupervised multiscale texture segmentation: determining adequate spatial and feature resolutions for different regions of the image, and utilizing information across different scales/resolutions. The center of a homogeneous texture is analyzed using coarse spatial resolution, and its border is detected using fine spatial resolution so as to locate the boundary accurately. The extraction of texture features is achieved via a multiresolution pyramid. The feature values are integrated across scales/resolutions adaptively. The number of textures is determined automatically using the variance ratio criterion. Experimental results on synthetic and real images demonstrate the improvement in performance of the proposed multiscale scheme over single scale approaches.

Image texture is a complex visual perception. With the ever-increasing spatial resolution of remotely sensed data, the role of image texture in image classification has increased. Current approaches to image texture analysis rely on a single band of spatial information to characterize texture. This paper presents a multiscale approach to image texture where first and second-order statistical measures were derived from different sizes of processing windows and were used as additional information in a supervised classification. By using several bands of textural information processed with different window sizes (from 5 5 to 15 15) the main forest stands in the image were improved up to a maximum of 40%. A geostatistical analysis indicated that there was no single window size that would adequately characterize the range of textural conditions present in this image. A number of different statistical texture measures were compared for this image. While all of the different texture measures provided a degree of improvement (from 4 to 13% overall), the multiscale approach achieved a higher degree of classification accuracy regardless of which statistical procedure was used. When compared with single band texture measures, the level of overall improvement varied between 4 and 8%. The results indicate that this multiscale approach is an improvement over the current single band approach to analysing image texture.

This study used geographic object-based image analysis (GEOBIA) with very high spatial resolution (VHR) aerial imagery (0.3 m spatial resolution) to classify vegetation, channel and bare mud classes in a salt marsh. Three classification issues were investigated in the context of segmentation scale: (1) a comparison of single- and multi-scale GEOBIA using spectral bands, (2) the relative benefit of incorporating texture derived from the grey-level co-occurrence matrix (GLCM) in classifying the salt marsh features in single- and multi-scale GEOBIA and (3) the effect of quantization level of GLCM texture in the context of multi-scale GEOBIA. The single-scale GEOBIA experiments indicated that the optimal segmentation was both class and scale dependent. Therefore, the single-scale approach produced an only moderately accurate classification for all marsh classes. A multi-scale approach, however, facilitated the use of multiple scales that allowed the delineation of individual classes with increased between-class and reduced within-class spectral variation. With only spectral bands used, the multi-scale approach outperformed the single-scale GEOBIA with an overall accuracy of 82% vs. 76% (Kappa of 0.71 vs. 0.62). The study demonstrates the potential importance of ancillary data, GLCM texture, to compensate for limited between-class spectral discrimination. For example, gains in classification accuracies ranged from 3% to 12% when the GLCM mean texture was included in the multi-scale GEOBIA. The multi-scale classification overall accuracy varied with quantization level of the GLCM texture matrix. A quantization level of 2 reduced misclassifications of channel and bare mud and generated a statistically higher classification than higher quantization levels. Overall, the multi-scale GEOBIA produced the highest classification accuracy. The multi-scale GEOBIA is expected to be a useful methodology for creating a seamless spatial database of marsh landscape features to be used for further geographic information system (GIS) analyses.

地物具有多尺度的特点,单一尺度难以准确描述遥感影像包含的地物纹理信息.利用我国自行研发的高分一号遥感影像数据,采用灰度共生矩阵对第一主成分进行纹理特征提取,利用Jeffries-Matusit距离选择多尺度组合,并通过单一纹理结合多光谱数据的分类精度,以及纹理特征间的相关性,最终选择多尺度纹理特征组合进行面向对象分类.研究结果表明:结合多尺度纹理特征组合的面向对象GF-1影像分类能有效提取地物信息,总体分类精度达到81.75％,Kappa系数0.78.

In this study, a new algorithm was proposed for edge extraction of greenhouse strawberry leaf in natural light based on the 4-level daubechies 5 ('db5') wavelet decomposition. This algorithm adopts different segmentation methods for the reconstructed images at different scales to erase the external background and the internal leaf vein interference. There were two advantages of this method. One...

高分辨率影像的广泛应用推进面向对象影像分析(OBIA)的发展,而分割作为面向对象分类的关键步骤,其尺度的选择直接关系到地物信息的提取。空间尺度是地物的固有属性,在合适的分割尺度下可以更好地挖掘地物信息。本文结合最大面积法和分割质量评价模型对张山营镇影像进行分割实验,先通过分析对象最大面积初步得到最优尺度范围,后结合分割质量评价模型以确定最优分割尺度层次。在此基础上,综合样本提取的光谱、纹理等特征进行规则训练,最终完成面向对象的土地覆被分类研究。结果显示:基于多层次最优尺度的规则分类方法获得更好的分类结果,其总体精度为88.8%,Kappa系数为0.861,而基于单一尺度的最邻近法总体精度81.4%,Kappa系数0.773,基于单一尺度的规则分类法总体精度为83.2%,Kappa系数为0.85。

<p>采用面向对象方法处理高空间分辨率遥感影像时,影像分割质量对后续影像的信息提取结果影响很大。本文主要针对高分辨率影像分割中地物多尺度的问题,提出了一种基于多层优选尺度的高分辨率影像分割算法。该算法首先采用一系列规律变化的尺度对高分辨率影像进行多尺度分割,然后通过单分割层全局标准差的变化与尺度的关系确定一组最优分割尺度。在此基础上,通过各优选分割层之间的包含关系,局部建立多层次对象树,从整体上形成影像森林;通过局部同质性异质性综合评价指数的比较及父层光谱特征的限制来选取多层次对象树中的优势对象,从而获得最终的高分辨率影像分割结果。最后,本文分别采用了Geoeye和ZY3多光谱影像进行了2组分割实验,结果表明本文算法能有效地提高正常分割影像对象的比例。</p>

This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons . Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.