共查询到19条相似文献，搜索用时 171 毫秒
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融合像素—多尺度区域特征的高分辨率遥感影像分类算法 总被引：1，自引：0，他引：1
针对基于像素多特征的高分辨率遥感影像分类算法的"胡椒盐"现象和面向对象影像分析方法的"平滑地物细节"现象,提出了一种融合像素特征和多尺度区域特征的高分辨率遥感影像分类算法。(1)首先采用均值漂移算法对原始影像进行初始过分割,然后对初始过分割结果进行多尺度的区域合并,形成多尺度分割结果。根据多尺度区域合并RMI指数变化和分割尺度对分类精度的影响,确定最优分割尺度。(2)融合光谱特征、像元形状指数PSI(Pixel Shape Index)、初始尺度和最优尺度区域特征,并对多类型特征进行归一化,最后结合支持向量机(SVM)进行分类。实验结果表明该算法既能有效减少基于像素多特征的高分辨率遥感影像分类算法的"胡椒盐"现象,又能保持地物对象的完整性和地物细节信息,提高易混淆类别(如阴影和街道,裸地和草地)的分类精度。 相似文献
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城市信息的提取是城市动态监测和分析的基础,而城市动态监测对社会发展和人类生活具有重要意义。本文基于三峡地区的SPOT-5遥感影像,以城市绿地和建筑物为研究对象,用ENVI FX影像处理软件,对实验区的绿地和建筑物进行多尺度影像分割信息提取。结果表明,采用多尺度分割技术提取高分辨率影像中地物的提取精度更高,并有效地避免了"椒盐现象"。 相似文献
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面向对象与卷积神经网络模型的GF-6 WFV影像作物分类 总被引：1，自引：0，他引：1
GF-6 WFV影像是中国首颗带有红边波段的中高分辨率8波段多光谱卫星的遥感影像,对于其影像及红边波段对作物分类影响的研究利用亟待展开。本文结合面向对象和深度学习提出一种适用于GF-6 WFV红边波段的卷积神经网络(RE-CNN)遥感影像作物分类方法。首先采用多尺度分割和ESP工具选择最佳分割参数完成影像分割,通过面向对象的CART决策树消除椒盐现象的同时提取植被区域,并转化为卷积神经网络的输入数据,最后基于Python和Numpy库构建的卷积神经网络模型(RE-CNN)用于影像作物分类及精度验证。有无红边波段的两组分类实验结果表明:在红边波段组,卷积神经网络(RE-CNN)作物分类识别取得了较好的效果,总体精度高达94.38%,相比无红边波段组分类精度提高了2.83%,验证了GF-6 WFV红边波段对作物分类的有效性。为GF-6 WFV红边波段影像用于作物的分类研究提供技术参考和借鉴价值。 相似文献
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针对高空间分辨率遥感影像中的地物具有多尺度特性,以及各个尺度的对象特征对地物分类精度的影响具有较强的尺度效性,并结合面向对象影像分析方法和多尺度联合稀疏表示方法在高空间分辨率遥感影像分类中的各自优点,提出了一种面向对象的多尺度加权稀疏表示的高空间分辨率遥感影像分类算法。首先,采用多尺度分割算法获得多尺度分割结果并提取对象的多尺度特征;然后,根据影像对象的多尺度分割质量测度计算各尺度的对象权重,构建面向对象的多尺度加权联合稀疏表示模型;最后,采用2个国产GF-2高空间分辨率遥感数据集和1个高光谱-高空间分辨率航空遥感数据集（WashingtonD.C.数据）验证该算法的有效性。试验结果表明,与SVM、像素级稀疏表示、单尺度和多尺度对象级稀疏表示和深度学习等算法相比较,本文算法获得了较高的OA和Kappa分类精度,提高了各个尺度地物的分类精度,有效抑止了地物分类结果中的椒盐噪声现象,同时保持大尺度地物的区域性和小尺度地物的细节信息。 相似文献
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基于多尺度分割的煤矿区典型地物遥感信息提取 总被引：1，自引：0，他引：1
根据煤矿区典型地物类型的特点,研究遥感影像信息提取时面向对象分类方法的最优分割尺度问题。试验结果表明:在适于不同地物提取的最优分割尺度下,充分利用煤矿区影像对象的光谱、形状、纹理以及类间相关等特征,并综合应用隶属函数法和最邻近分类法,能有效地提取出煤矿区地物信息,与最大似然分类法相比,能够较好地消除"椒盐现象",其总体分类精度可提高26.2%。 相似文献
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基于对象级的ADS40遥感影像分类研究 总被引：1，自引：0，他引：1
针对ADS40影像的空间分辨率高而光谱分辨率相对不足的特点,提出了一种基于多尺度分割的对象级遥感分类方法.首先通过多尺度分割获得影像对象,然后利用对象所包含的光谱特征、几何特征、拓扑特征来确定地物识别中可能要用到的各种特征参数,并建立对象间的分类层次结构图,最后利用模糊分类器逐级分层分类来提取地物信息.研究结果表明,面向对象的分类方法与传统方法相比,可显著提高分类精度,有效抑制"椒盐现象"的产生,更加适合于几何信息和结构信息丰富的ADS40影像的自动识别分类.通过对太原市ADS40影像进行分类验证了此方法的有效性. 相似文献
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为了提高农业遥感数据处理中多光谱影像分割的精度,文章提出了一种面向农田信息提取的遥感影像分割算法:利用KMeans非监督分类算法和Fisher标准估算多光谱遥感影像中各个波段的权值,并将估算的波段权值应用到光谱合并计算中,能够较好地提高农田区域的分割精度,实现基于全局最优合并的区域生长算法,得到最优化的分割结果;从分割结果中提取基于区域的NDVI信息可以较为快速、准确地区分农田和非农田区域。实验结果说明:该方法的分割精度优于传统的全局最优合并算法和FNEA算法,并对遥感影像中旱田和水田的提取均有较好的效果。 相似文献
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With the availability of very high resolution multispectral imagery, it is possible to identify small features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult task. This paper demonstrates the potential of 8 bands capability of World View 2 satellite for better automated feature extraction and discrimination studies. Multiresolution segmentation and object based classification techniques were then applied for discrimination of urban and vegetation features in a part of Dehradun, Uttarakhand, India. The study demonstrates that scale, colour, shape, compactness and smoothness have a significant influence on the quality of image objects achieved, which in turn governs the classified result. The object oriented analysis is a valid approach for analyzing high spatial and spectral resolution images. World View 2 imagery with its rich spatial and spectral information content has very high potential for discrimination of the less varied varieties of vegetation. 相似文献
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为了解决多尺度遥感图像变化检测在降噪时丢失大量高频信息及单一像素孤立性的问题,提出了一种双树复小波变换DT-CWT(Dual-tree Complex Wavelet Transform)和马尔可夫随机场MRF(Markov Random Field)相结合的非监督遥感图像变化检测算法,首先采用DT-CWT对差异图像进行多尺度分解,并根据MRF模型分割算法提取高频区域的变化特征,然后进行相应层的高、低频重构,再对重构后的各层建立MRF模型并根据贝叶斯最大后验概率准则MAP(Maximum A Posterior)进行最终分割,最后对各层分割结果进行求交融合,得到最终的变化检测结果掩膜图。对比实验结果表明,该方法在去除杂点和噪声的同时能够较好地保留高频信息,并且边缘检测更加平滑,具有较高的变化检测精度和很好的鲁棒性。 相似文献
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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
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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
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With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images. 相似文献
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This paper is an exploratory study, which aimed to discover the synergies of data fusion and image segmentation in the context of EO-based rapid mapping workflows. Our approach pillared on the geographic object-based image analysis (GEOBIA) focusing on multiscale, internally-displaced persons’ (IDP) camp information extraction from very high spatial resolution (VHSR) images. We applied twelve pansharpening algorithms to two subsets of a GeoEye-1 image scene that was taken over a former war-induced ephemeral settlement in Sri Lanka. A multidimensional assessment was employed to benchmark pansharpening algorithms with respect to their spectral and spatial fidelity. The multiresolution segmentation (MRS) algorithm of the eCognition Developer software served as the key algorithm in the segmentation process. The first study site was used for comparing segmentation results produced from the twelve fused products at a series of scale, shape, and compactness settings of the MRS algorithm. The segmentation quality and optimum parameter settings of the MRS algorithm were estimated by using empirical discrepancy measures. Non-parametric statistical tests were used to compare the quality of image object candidates, which were derived from the twelve pansharpened products. A wall-to-wall classification was performed based on a support vector machine (SVM) classifier to classify image objects candidates of the fused images. The second site simulated a more realistic crisis information extraction scenario where the domain expertise is crucial in segmentation and classification. We compared segmentation and classification results of the original images (non-fused) and twelve fused images to understand the efficacy of data fusion. We have shown that the GEOBIA has the ability to create meaningful image objects during the segmentation process by compensating the fused image’s spectral distortions with the high-frequency information content that has been injected during fusion. Our findings further questioned the necessity of the data fusion step in rapid mapping context. Bypassing time-intensive data fusion helps to actuate EO-based rapid mapping workflows. We, however, emphasize the fact that data fusion is not limited to VHSR image data but expands over many different combinations of multi-date, multi-sensor EO-data. Thus, further research is needed to understand the synergies of data fusion and image segmentation with respect to multi-date, multi-sensor fusion scenarios and extrapolate our findings to other remote sensing application domains beyond EO-based crisis information retrieval. 相似文献
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B. Poovalinga Ganesh S. Rajendran A. Thirunavukkarasu K. Maharani 《Journal of the Indian Society of Remote Sensing》2009,37(1):1-8
Uncertainties in Geovisualaization / GIScience spatial data can minimize but not completely provided by the different image
processing classification methods. The methods of image processing techniques are purely dependent on spectral signature values.
In the present study, we collected end member spectral values from both satellite data and field signatures and applied in
supervised and fuzzy classification of image processing techniques to discriminate the iron ore formations and associated
land cover features of part of Godumalai hill region of Salem District, Tamil Nadu State, India. The result of analysis shows
that the fuzzy classified image discriminated the iron formation with better appearance and distinct boundary between the
associated features than the analyses results obtained by supervised methods. 相似文献