共查询到20条相似文献，搜索用时 171 毫秒
1.
针对海面舰船等具有一定空间稀疏性的合成孔径雷达成像场景,提出了一种稀疏场景目标的距离像峰值聚类分割成像方法。首先采用小波降噪算法对SAR原始回波数据进行预处理,通过距离压缩和距离徙动校正获得不同观测位置的距离像,利用基于二阶差分算子的快速峰值检测算法检测距离像峰值点,对峰值检测结果距离维聚类后方位向成像,实现了距离向能量区间稀疏目标的分割成像;对峰值检测结果距离-方位二维聚类后方位向成像,实现了距离向能量区间与方位向合成孔径时间无耦合稀疏目标的分割成像。仿真结果表明,对海面舰船等具有空间稀疏性的成像场景,所提方法能够实现目标的有效分割成像,不仅在完整保留目标回波信息的同时大幅度地降低了方位向压缩的运算量,而且分割成像结果更有利于目标的快速识别。 相似文献
2.
《国土资源遥感》2017,(2)
在遥感图像分割中,植被是重要的一类对象,植被细分割一般有3个目标,按尺度分为乔木、灌木和草与苔藓。针对单一层次多分类方法不能充分利用植被目标不同纹理尺度实现精确的多分类问题,提出了一种基于谱直方图的遥感图像分层次、多尺度植被分割方法。首先用归一化植被指数(normalized difference vegetation index,NDVI)提取出遥感图像中的植被区域,然后再对该区域分层实现多个二分类算法、完成多分类操作。在每个分类层次,利用目标的先验知识和纹理尺度选择纹理滤波参数,对滤波结果提取各子块图像的谱直方图用以表达纹理特征,从而实现1个层次的分割。实验结果表明,该方法较好地利用了植被各层次目标的先验知识和纹理尺度,使得对纹理滤波器的增强处理更具针对性;谱直方图的特征区分度更大,使得植被细分割精度明显提高。 相似文献
3.
随着数字城市的发展,城市三维模型重建对三维点云结构化的需求与精度要求越来越高。如何有效准确地分割室内语义模型与三维重构是当前研究的热点问题。点云分割分类是室内点云结构化的重要基础,如何将粘连点云构件进行准确分割并用于室内点云结构化,是当前城市建模的难点。本文提出了一种面向室内粘连点云数据的分割分类方法。首先,利用深度学习网络处理室内点云数据;其次,对点云数据进行标签分类,得到目标标签点云;然后,利用欧氏算法对目标点云进行聚类分割,通过室内语义构件包围盒信息计算各目标中心点坐标与水平半径;最后,利用点云最小割实现室内粘连点云的准确分割。利用3组室内场景中获取的数据对分割方法的精度及有效性进行了验证。结果表明,该分割优化方法具有较高的精度与数据完整性。 相似文献
4.
矢量C-V模型的高光谱遥感影像分割 总被引：1，自引：0，他引：1
高光谱遥感影像除了包含普通2维影像所具有的空间信息还包含了1维光谱信息,传统的针对2维影像的分割方法不能很好地应用于高光谱遥感影像。为此,本文提出一种能够同时处理多波段影像的高光谱遥感影像矢量C-V模型分割方法。首先选出高光谱遥感影像中目标与背景对比度较大的波段,并通过计算波段相关系数,去除其中的冗余信息形成新的波段组合,进而根据所确定的波段组合构建高光谱遥感影像矢量矩阵;在此基础上,构造基于该矢量矩阵的矢量C-V分割模型。模型中通过引入基于梯度的边缘引导函数,在保留传统C-V模型基于区域信息进行影像分割的基础上,利用影像的边缘细节信息,增强了模型在异质区域和复杂背景情况下对目标边缘的捕捉能力,提高了对高光谱遥感影像的分割精度和速度。最后利用HYPERION数据进行仿真实验,并将实验结果和传统C-V模型和相关方法进行了对比,结果表明,本文方法能够在短时间内有效地分割高光谱遥感影像,与传统方法相比,具有分割精度更高运算速度更快的特点。 相似文献
5.
近几年来，离散小波变换在遥感图像压缩、消噪和融合中得到了广泛的应用。利用航空飞行的高光谱图像数据，先后进行了主成分分析及10种小波变换，并应用分类回归树对其进行分类处理。将小波变换的分类结果与主成分分析的结果及不同的小波变换方法之间进行了对比。结果表明，在样本数相同的条件下，小波变换的分类精度均高于主成分分析，其中Haar小波的分类精度最高；小波变换后的分类对样本数量的要求要小于主成分分析。在样本数足够的情况下，主成分分析数据压缩率要高于小波变换。但小波变换在压缩的情况下，仍保留了原光谱的大部分信息。而主成分分析只保留了原图像的方差而无法保留波形。 相似文献
6.
一种基于对象和多种特征整合的分类识别方法研究 总被引：8，自引：0，他引：8
遥感图像空间分辨率的提高，为目标物的纹理特征和形状特征的提取提供了客观基础，同时也使得传统的基于像元的分类识别方法受到了严重的挑战。因此，需要对传统的方法进行改进或发展新的方法。本文采用面向对象的分析思想，通过图像分割和分割对象的矢量化等一系列的预处理，并在此基础上实现了目标形状信息的提取，最后综合利用光谱特征和形状特征应用模糊分类器实现两种典型的人造目标的分类提取实验。识别的精度评价主要通过目视解译完成。分析表明，形状信息的提取大大丰富了目标识别的特征库，尤其在感兴趣目标与背景物具有相近的光谱反应而形状特征有明显差异的条件下，这种利用光谱与形状特征整合的提取方法能够大大提高目标的识别精度。 相似文献
7.
针对卫星视频的运动检测存在的局部伪运动虚警问题,提出一种决策树支持的局部有差别更新背景减除法。首先,对ViBe背景模型加以改进,在原模型基础上新增更新因子参数,建立初始的背景模型;其次,利用改进的模型进行运动检测,并对检测结果进行连通域标记,得到分割目标集;然后,根据目标特征属性建立决策树模型,并对分割目标集进行弱分类处理;最后,根据分类结果对背景模型样本和对应的更新因子进行局部有差别更新,进而实现“伪运动”虚警消除。利用SkySat和吉林一号视频数据进行实验,结果表明所提方法的效果和性能良好,检测率优于0.909,针对经典ViBe方法的误检测消除率优于90%。 相似文献
8.
针对利用TM影像进行土地利用传统分类精度不高的问题,该文提出了一种综合应用影像纹理与光谱特征对TM影像进行土地利用模糊分类的方法。采用主成分分析法对研究流域TM影像的光谱及纹理特征信息进行压缩与融合,并对融合后的TM影像数据进行3个组别的多尺度分割,在影像分割对象单元的基础上应用面向对象的模糊逻辑隶属度函数法实现影像的软语义分类。相对传统分类方法而言,该方法在充分利用影像光谱信息的基础上综合了影像的纹理信息,且分类理论思想更加符合人们对于客观事物的认知规律,分类精度有了显著的提高,为TM影像分类方法的改进提供一定的参考。 相似文献
9.
提出了一种基于压缩感知(compressed sensing,CS)和恒虚警率(constant false alarm rate,CFAR)目标检测算法,用于合成孔径雷达(synthetic aperture radar,SAR)图像的目标检测。针对传统的均值类和有序统计量类CFAR目标检测算法,首先对每个局部滑窗的背景杂波像素利用压缩感知进行重建,以此来降低SAR图像相干斑现象的影响,然后利用重建后的数据进行杂波分布参数的估计,并利用CFAR检测器进行目标检测。在真实的SAR图像中证明了上述目标检测算法的有效性。 相似文献
10.
针对基于LiDAR点云数据进行建筑物自动重建中存在的数据冗余问题,该文设计了一种定量描述激光点位于地物边缘区几率大小的指标——边缘系数,并据此提出了基于边缘系数的建筑物LiDAR点云数据简化方法。该方法利用激光点与其邻域点的位置、数量及分布计算该点的边缘系数,通过试验分析确定边缘系数的阈值并对点云数据进行分割,最后保留建筑物边缘区域的点,实现点云数据的简化。实验表明,该方法在对点云数据进行高效压缩的同时有效保留了位于地物边缘处的点云,有助于提高海量点云数据处理能力和建筑物重建效率。 相似文献
11.
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
12.
13.
Lossy compression is being increasingly used in remote sensing; however, its effects on classification have scarcely been studied. This paper studies the implications of JPEG (JPG) and JPEG 2000 (J2K) lossy compression for image classification of forests in Mediterranean areas. Results explore the impact of the compression on the images themselves as well as on the obtained classification. The results indicate that classifications made with previously compressed radiometrically corrected images and topoclimatic variables are not negatively affected by compression, even at quite high compression ratios. Indeed, JPG compression can be applied to images at a compression ratio (CR, ratio between the size of the original file and the size of the compressed file) of 10:1 or even 20:1 (for both JPG and J2K). Nevertheless, the fragmentation of the study area must be taken into account: in less fragmented zones, high CR are possible for both JPG and J2K, but in fragmented zones, JPG is not advisable, and when J2K is used, only a medium CR is recommended (3.33:1 to 5:1). Taking into account that J2K produces fewer artefacts at higher CR, the study not only contributes with optimum CR recommendations, but also found that the J2K compression standard (ISO 15444-1) is better than the JPG (ISO 10918-1) when applied to image classification. Although J2K is computationally more expensive, this is no longer a critical issue with current computer technology. 相似文献
14.
一种区域自适应的遥感影像分水岭分割算法 总被引：4，自引：0，他引：4
提出了一种区域自适应的标记分水岭分割方法。该方法利用高斯低通滤波和概率统计相结合的方法,对梯度影像进行区域自适应阈值分割,提取分割标记,然后采用Meyer算法进行标记分水岭分割。实验结果表明,该算法能够有效解决遥感影像不同区域的分割问题,达到比较理想的水平。 相似文献
15.
为了提高大区域土地利用变化信息提取效率,本文在变化信息自动发现的基础上,基于目标灰度信息,研究遥感影像中土地利用变化信息的边界提取方法.由于研究对象是大区域中的小目标,无法直接利用灰度直方图寻找分割目标的灰度分布范围.本文提出了一种典型区域法,方法是利用区域中分割目标密集的多个典型小区域的灰度直方图计算所有目标的分割阈值.由于影像中地物众多,为了减少噪声的影响,提高计算速度,先利用双阈值分割法对目标进行粗分割,再对其进行小波变换,利用其模值,以及结合目标灰度信息,可以将目标边界快速地提取出来. 相似文献
16.
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
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.
基于面向对象和规则的遥感影像分类研究 总被引：54，自引：4，他引：54
讨论了面向对象和规则的光学遥感影像分类方法。首先利用多尺度分割形成影像对象，建立对象的层次结构，计算对象的光谱特征、几何特征、拓扑特征等，利用对象、特征形成分类规则，并通过不同对象层间信息的传递和合并实现对影像的分类。并以北京城市土地利用分类为例，对该方法进行了验证。 相似文献
19.
基于数学形态学的遥感影像水域提取方法 总被引：2，自引：1，他引：1
利用数学形态学的方法对遥感影像上的水体进行了提取实验。经实验结果得出,利用数学形态学的基本运算组合成的各种算子:高、低帽变换、滤波与阈值截取相结合的增强方法,通过选择合适的结构元素,对图像进行预处理后进行分割,可以完成遥感影像上灰度变化较小的水体提取,其结果优于直接对原图像进行形态梯度法、均值分割的结果,对分割后的结果进行矢量跟踪后,得出的结果可以直接应用到GIS中去。 相似文献
20.
J. B. K. Kiema 《The Photogrammetric Record》2000,16(96):997-1006
Investigation of the influence of data compression techniques on the quality and further processing of satellite sensor imagery is a research and development topic which remains both current and important. In principle, this extends the scope of data compression in remote sensing beyond the classical applications of data transmission and storage. Against this background, the main objective of this study is to examine the effect of data compression on the automatic classification of Landsat imagery. The main focus of this is on the built environment. In order to obtain better segmentation results, the feature base is expanded to include both spectral features, such as spectral signature and texture, and non-spectral features such as shape, structure and topology. This expansion enables the integration of context information into the feature extraction process. The image is systematically compressed (channel by channel) at compression rates ranging from 5 to 100 using wavelet-based software. An integrated, pixel-based classification approach is then used to segment the compressed imagery. The analysis of the results obtained indicates that a compression rate of up to 20 can conveniently be used without adversely affecting the segmentation results. The suitability of wavelet compression schemes for the compression of heterogeneous data is also underlined. 相似文献