共查询到20条相似文献，搜索用时 710 毫秒
1.
A. Mohan Vamsee P. Kamala Tapas R. Martha K. Vinod Kumar G. Jai sankar E. Amminedu 《Journal of the Indian Society of Remote Sensing》2018,46(1):31-41
Image segmentation to create representative objects by region growing image segmentation techniques such as multi resolution segmentation (MRS) is mostly done through interactive selection of scale parameters and is still a subject of great research interest in object-based image analysis. In this study, we developed an optimum scale parameter selector (OSPS) tool for objective determination of multiple optimal scales in an image by MRS using eCognition software. The ready to use OSPS tool consists of three modules and determines optimum scales in an image by combining intrasegment variance and intersegment spatial autocorrelation. The tool was tested using WorldView-2 and Resourcesat-2 LISS-IV Mx images having different spectral and spatial resolutions in two areas to find optimal objects for ground features such as water bodies, trees, buildings, road, agricultural fields and landslides. Quality of the objects created for these features using scale parameters obtained from the OSPS tool was evaluated quantitatively using segmentation goodness metrics. Results show that OSPS tool is able determine optimum scale parameters for creation of representative objects from high resolution satellite images by MRS method. 相似文献
2.
Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing ‘optimal segmentation’. Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved. 相似文献
3.
Angelos Tzotsos Konstantinos Karantzalos Demetre Argialas 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(1):2-16
In this research, an object-oriented image classification framework was developed which incorporates nonlinear scale-space filtering into the multi-scale segmentation and classification procedures. Morphological levelings, which possess a number of desired spatial and spectral properties, were associated with anisotropically diffused markers towards the construction of nonlinear scale spaces. Image objects were computed at various scales and were connected to a kernel-based learning machine for the classification of various earth-observation data from both active and passive remote sensing sensors. Unlike previous object-based image analysis approaches, the scale hierarchy is implicitly derived from scale-space representation properties. The developed approach does not require the tuning of any parameter—of those which control the multi-scale segmentation and object extraction procedure, like shape, color, texture, etc. The developed object-oriented image classification framework was applied on a number of remote sensing data from different airborne and spaceborne sensors including SAR images, high and very high resolution panchromatic and multispectral aerial and satellite datasets. The very promising experimental results along with the performed qualitative and quantitative evaluation demonstrate the potential of the proposed approach. 相似文献
4.
一种融合超像素与最小生成树的高分辨率遥感影像分割方法 总被引：4，自引：3，他引：1
影像分割是面向对象高分辨率遥感影像分析的基础与关键。针对传统影像分割方法易受噪声影响,且难以确定合适的影像分割尺度的问题,本文提出了一种融合超像素与最小生成树的高分辨率遥感影像分割方法。首先用简单线性迭代聚类算法对影像进行过分割生成超像素;然后初始设定影像分割数,采用区域动态约束聚类算法对超像素进行合并,获得分割数-方差和、分割数-局部方差、分割数-局部方差变化率指标图,依据3个指标图确定合适的影像分割数;最后根据确定的合适影像分割数,采用区域动态约束聚类算法对超像素重新合并得到分割结果。定性对比试验和定量评价结果表明,本文方法可以有效地克服影像噪声对分割结果的影响,获得良好的影像分割结果。 相似文献
5.
The results obtained using the object-based image analysis approach for remote sensing image analysis depend strongly on the quality of the segmentation step. In this paper, to optimize the scale parameter in a multiresolution segmentation, we analyse a high-resolution image of a large and heterogeneous agricultural area. This approach is based on using a set of agricultural plots extracted from official maps as uniform spatial units. The scale parameter is then optimized in each uniform spatial unit. Intra-object and inter-object heterogeneity measurements are used to evaluate each segmentation. To avoid subsegmentation, some oversegmentation is allowed, but is attenuated in a second step using the spectral difference segmentation algorithm. The statistical distribution of the scale parameter is not equal in all land uses, indicating the soundness of this local approach. A quantitative assessment of the results was also conducted for the different land covers. The results indicate that the spectral contrast between objects is larger with the local approach than with the global approach. These differences were statistically significant in all land uses except irrigated fruit trees and greenhouses. In the absence of subsegmentation, this suggests that the objects will be placed far apart in the space of variables, even if they are very close in the physical space. This is an obvious advantage in a subsequent classification of the objects. 相似文献
6.
7.
Segmentation algorithms applied to remote sensing data provide valuable information about the size, distribution and context of landscape objects at a range of scales. However, there is a need for well-defined and robust validation tools to assessing the reliability of segmentation results. Such tools are required to assess whether image segments are based on ‘real’ objects, such as field boundaries, or on artefacts of the image segmentation algorithm. These tools can be used to improve the reliability of any land-use/land-cover classifications or landscape analyses that is based on the image segments.The validation algorithm developed in this paper aims to: (a) localize and quantify segmentation inaccuracies; and (b) allow the assessment of segmentation results on the whole. The first aim is achieved using object metrics that enable the quantification of topological and geometric object differences. The second aim is achieved by combining these object metrics into a ‘Comparison Index’, which allows a relative comparison of different segmentation results. The approach demonstrates how the Comparison Index CI can be used to guide trial-and-error techniques, enabling the identification of a segmentation scale H that is close to optimal. Once this scale has been identified a more detailed examination of the CI–H- diagrams can be used to identify precisely what H value and associated parameter settings will yield the most accurate image segmentation results.The procedure is applied to segmented Landsat scenes in an agricultural area in Saxony-Anhalt, Germany. The segmentations were generated using the ‘Fractal Net Evolution Approach’, which is implemented in the eCognition software. 相似文献
8.
Image segmentation is one of key steps in object based image analysis of very high resolution images. Selecting the appropriate scale parameter becomes a particularly important task in image segmentation. In this study, an unsupervised multi-band approach is proposed for scale parameter selection in the multi-scale image segmentation process, which uses spectral angle to measure the spectral homogeneity of segments. With the increasing scale parameter, spectral homogeneity of segments decreases until they match the objects in the real world. The index of spectral homogeneity is thus used to determine multiple appropriate scale parameters. The performance of the proposed method is compared to a single-band based method through qualitative visual interpretation and quantitative discrepancy measures. Both methods are applied for segmenting two images: a QuickBird scene of an urban area within Beijing, China and a Woldview-2 scene of a suburban area in Kashiwa, Japan. The proposed multi-band based segmentation scale parameter selection method outperforms the single-band based method with the better recognition for diverse land cover objects in different urban landscapes. 相似文献
9.
由于全变分(Total Variation,TV)模型具有较好的去噪、增强和扩散等功能,在过去的几十年中,TV模型在图像去噪、增强和超分辨率重建等方面得到了深入研究与广泛应用。鉴于TV模型的理论与分割理论具有一致性,因此本文主要研究TV模型用于高分辨率遥感影像的分割,并针对地物多尺度特征,提出了自适应的TV(ATV)模型;且与目前流行的面向对象的影像分析软件eCognition中的FNEA分割方法进行了比较。实验采用2幅高分辨率遥感影像,同时采用了面向对象的分割和分类评价,得出各方法各具优缺点的结论。 相似文献
10.
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
11.
Lu Zhang Xueyan HeTimo Balz Xiaohong WeiMingsheng Liao 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(1):133-145
As a kind of generic sensor model, the rational function model (RFM) has been widely used in geometric processing of optical images, but has not yet been applied to SAR datasets. In this article the feasibility and methodology of rational function (RF) modeling for SAR datasets are investigated. After a review of the mathematic formulation of the RF model and the Range-Doppler model for SAR systems, the feasibility of applying RFM to SAR datasets is analyzed. Afterwards a two-stage approach is proposed as the key technique for SAR RF modeling to solve unknown parameters of RFM in a fast and unbiased way. The effectiveness and advantages of this approach are demonstrated by comparisons with traditional methods. Experimental results obtained for various spaceborne SAR datasets of different processing levels show that RFM is a suitable replacement of the rigorous Range-Doppler model for spaceborne SAR images. Furthermore, the impacts of several factors including the control point grid size, the number of elevation layers, and the orbit precision on SAR RFM solutions are evaluated quantitatively. The results show that the number of elevation layers is a key factor in SAR RF modeling, and its value should be set carefully according to terrain conditions of study areas. Finally, potential applications of SAR RFM are discussed in brief. 相似文献
12.
面向对象的成都平原多源遥感影像分割尺度研究 总被引：1，自引：0，他引：1
要对高分辨率遥感影像进行分类,采用面向对象的遥感影像分析技术比传统的面向像元的遥感影像分析技术优越。要使用面向对象的遥感影像分析技术,关键的第一步是要对遥感影像进行分割,以便得到一系列与地物有密切联系的影像对象。分割的准确性与分割的尺度选择有关。本文针对成都平原高分辨率卫星影像分割尺度选择进行试验和研究,采用不同尺度对试验区不同分辨率遥感影像进行影像分割,并比较分割结果,得出成都平原高分辨率遥感影像数据分割最佳尺度与影像对象亮度均值标准差最大值所对应的分割尺度一致;并且遥感影像空间分辨率越高,最佳分割尺度越大,反之亦然。 相似文献
13.
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
14.
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。 相似文献
15.
GF-2影像面向对象典型城区地物提取方法 总被引：5，自引：3，他引：2
国产高分遥感影像信息丰富,提供了精准的地物空间细节,深入研究高分数据处理及其提取城区地类目标信息的方法具有重要意义。本文以国产高分二号（GF-2）遥感影像为数据源,利用规则集的面向对象分类方法,通过ESP尺度分析工具选取得出最优分割尺度,建立各类地物的特征体系及分类规则,最终提取出研究区典型城区地物信息,并将之与传统基于像元的SVM监督分类结果作比较。结果表明：规则集的面向对象分类总体精度为92.23%,Kappa系数为0.9,比SVM监督分类有大幅度提高。对高分二号等高分辨率影像,面向对象的分类方法精度更高,图示效果更好,是城区地物提取的有效方法。 相似文献
16.
受海冰自身特性、成像系统特性和环境因素的影响,合成孔径雷达SAR海冰图像具有非平稳、尺度依赖的空间结构,现有的单马尔可夫随机场MRF模型分割方法只能较好地适应非平稳性,对海冰场景的多尺度结构考虑仍然是全局的。为此,本文提出了一种区域分裂过程与二叉树分层结构自适应更新相结合的单MRF图像分割方法。首先利用单MRF模型的全局迭代权值完成初始区域合并,同时以二叉树形式保护合并过程的记录。所设计的分层合并算法可保证二叉树结构的节点数与场景中的对象尺度具有正相关性。随后的细化分裂并不产生新的区域,只是返回到初始配置。依据场景中不同区域对象的尺度,自适应地调整空间语境模型中的尺度权值,实现区域更新。实验表明,该方法有效提高了带有多尺度结构SAR海冰场景的分割精度。 相似文献
17.
为解决高分影像分割的边缘锯齿性明显等问题，本文以黑龙江省伊春市桦皮羌子林场为研究区开展了有无多光谱数据辅助的高分影像分割对比试验。首先，本文设计了多尺度分割算法的相同尺度参数下分割试验，确定了该算法分割GF-2影像时应采用的最佳同质性准则组合参数；然后，基于影像分割对象同质性局部方差变化率反映最优分割尺度的思想，利用ESP2工具找出固定尺度范围内的最优分割尺度范围；最后执行最佳同质性准则组合参数配合下的最优分割尺度范围内各个尺度下的多尺度分割，并采用矢量距离指数、紧密度指数、形状指数对2种分割试验结果进行了评价。结果表明，与GF-2影像独立分割相比，Landsat 8多光谱数据辅助下的GF-2影像分割在矢量距离指数、紧密度指数、形状指数的质量上均有提升，平均提升率分别为8.05%、28.40%、11.76%。 相似文献
18.
利用小波变换的高分辨率多光谱遥感图像多尺度分水岭分割 总被引：4，自引：2，他引：2
为了减少仅用分水岭变换而导致的过分割问题,本文提出利用小波变换的多尺度处理方式用于融合后多光谱QuickBird图像的分割。整个分割过程包括多尺度图像表示、图像分割、区域合并和结果映射等过程。首先,依据原始图像的大小确定分解尺度并用小波变换产生各波段的低尺度图像。采用相位一致模型提取各近似系数的梯度,并逐尺度地融合各梯度图。分析不同尺度下的不同地物的局部梯度方差,以选择最佳的小波分解尺度。然后,通过移动阈值与扩展最小变换,利用多层次标记提取方法标记均质区域。进而,在梯度重建的基础上利用标记分水岭变换得到分割图像。其次,采取空间相邻关系、面积、光谱与纹理等多约束策略,以搜索最小合并代价的方式合并最初分割区域中的邻接区域对。最后,修改细节子图并进行小波逆变换将最初分割结果投影到更高尺度图像,同时处理边界上的像元以保持区域边界直至原始图像。实验结果表明本文方法不仅能够用于高分辨率多光谱遥感图像的分割,而且缓解了过分割问题且取得了较准确的分割效果。 相似文献
19.
Benefiting from multi-constellation Global Navigation Satellite Systems (GNSS), more and more visible satellites can be used to improve user positioning performance. However, due to limited tracking receiver channels and power consumption, and other issues, it may be not possible, or desirable, to use all satellites in view for positioning. The optimal subset is generally selected from all possible satellite combinations to minimize either Geometric Dilution of Precision (GDOP) or weighted GDOP (WGDOP). However, this brute force approach is difficult to implement in real-time applications due to the time- and power-consuming calculation of the DOP values. As an alternative to a brute force satellite selection procedure, the authors propose an end-to-end deep learning network for satellite selection based on the PointNet and VoxelNet networks. The satellite selection is converted to a satellite segmentation problem, with specified input channel for each satellite and two class labels, one for selected satellites and the other for those not selected. The aim of the satellite segmentation is that a fixed number of satellites with the minimum GDOP/WGDOP value can be segmented from any feeding order of input satellites. To validate the proposed satellite segmentation network, training and test data from 220 IGS stations tracking GPS and GLONASS satellites were used. The segmentation performance using different architectures and representations of input channels, including receiver-to-satellite unit vector and elevation and azimuth, were compared. It was found that the input channel with elevation and azimuth can achieve better performance than using the receiver-to-satellite unit vector, and an architecture with stacked feature encoding (FE) layers has better satellite segmentation performance than one without stacked FE layers. In addition, the models with GDOP and WGDOP criteria for selecting 9 and 12 satellites were trained. It was demonstrated that the satellite segmentation network was about 90 times faster than using the brute force approach. Furthermore, all the trained models can effectively select the satellites making the most contribution to the desired GDOP/WGDOP value. Approximately 99% of the tests had GDOP and WGDOP value differences smaller than 0.03 and 0.2, respectively, between the predicted subset and the optimal subset. 相似文献
20.
结合对象关系特征的高分辨率卫星影像建筑物识别方法 总被引：1，自引：0，他引：1
基于面向对象特征影像分析的思想,提出了一种结合建筑物和阴影对象邻近关系特征的建筑物识别提取方法。在多尺度影像
分割的基础上,利用对象的光谱和形状等特征,建立简单的分类决策树,提取粗略的建筑物候选区和相对准确的阴影区。计算相邻
近阴影对象和建筑物对象的关系特征,建立简单的知识规则,即可从建筑物候选区中消除广场等噪音,获得准确有效的建筑物目标
信息。通过QuickBird卫星影像的实验,证明了该方法在高分辨率卫星影像建筑物目标识别中具有相当的适用性和准确性。 相似文献