共查询到20条相似文献，搜索用时 671 毫秒
1.
针对腹部CT图像中组织分割的问题,提出了一种基于图割与改进的快速水平集的交互式分割方法。首先对人工给定的一个待分割目标的初始轮廓作膨胀运算,将所得内部边界所有像素点作为源点、外部边界像素点作为汇点构造图,并通过图割方法对CT图像进行初步分割,然后以膨胀所得内部边界作为初始轮廓,通过基于区域竞争主动轮廓模型（RCAC）的... 相似文献
2.
基于二维小波变换的FMI图象分割 总被引：1，自引：0，他引：1
为了从FMI资料中定量提取参数，一个重要的步骤是从实际FMI资料中分离出反映溶孔、溶洞、裂缝的子图像。本文给出的方法，考虑图像像元邻域的特征，应用二维小波变换求出目标与背景边缘的点集，按这个边缘点集的坐标点所对应的原图像像素灰度值的平均值作为分割阈值进行图像分割。实际资料处理表明，应用这种方法可以从实际的FMI资料中准确地分割出孔洞、裂缝的子图像并且可以按深度段连续自动处理，为后续定量计算参数奠定了良好基础。 相似文献
3.
P. E. Carbonneau B. Belletti M. Micotti B. Lastoria M. Casaioli S. Mariani G. Marchetti S. Bizzi 《地球表面变化过程与地形》2020,45(13):3120-3140
An estimated 76% of global stream area is occupied by channels with widths above 30 m. Sentinel-2 imagery with resolutions of 10 m could supply information about the composition of river corridors at national and global scales. Fuzzy classification models that infer sub-pixel composition could further be used to compensate for small channel widths imaged at 10 m of spatial resolution. A major challenge to this approach is the acquisition of suitable training data useable in machine learning models that can predict land-cover type information from image radiance values. In this contribution, we present a method which combines unmanned aerial vehicles (UAVs) and Sentinel-2 imagery in order to develop a fuzzy classification approach capable of large-scale investigations. Our approach uses hyperspatial UAV imagery in order to derive high-resolution class information that can be used to train fuzzy classification models for Sentinel-2 data where all bands are super-resolved to a spatial resolution of 10 m. We use a multi-temporal UAV dataset covering an area of 5.25 km2. Using a novel convolutional neural network (CNN) classifier, we predict sub-pixel membership for Sentinel-2 pixels in the fluvial corridor as divided into classes of water, vegetation and dry sediment. Our CNN model can predict fuzzy class memberships with median errors from −5% to +3% and mean absolute errors from 10% to 20%. We also show that our CNN fuzzy predictor can be used to predict crisp classes with accuracies from 95.5% to 99.9%. Finally, we use an example to show how a fuzzy CNN model trained with localized UAV data can be applied to longer channel reaches and detect new vegetation growth. We therefore argue that the novel use of UAVs as field validation tools for freely available satellite data can bridge the scale gap between local and regional fluvial studies. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd 相似文献
4.
Sun ZhongPing Shen WenMing Wei Bin Liu XiaoMan Su Wei Zhang Chao Yang JianYu 《中国科学:地球科学(英文版)》2010,53(1):34-44
The object-oriented information extraction technique was used to improve classification accuracy, and addressed the problem that HJ-1 CCD remote sensing images have only four spectral bands with moderate spatial resolution. We used two key techniques: the selection of optimum image segmentation scale and the development of an appropriate object-oriented information extraction strategy. With the principle of minimizing merge cost of merging neighboring pixels/objects, we used spatial autocorrelation index Moran’s I and the variance index to select the optimum segmentation scale. The Nearest Neighborhood (NN) classifier based on sampling and a knowledge-based fuzzy classifier were used in the object-oriented information extraction strategy. In this classification step, feature optimization was used to improve information extraction accuracy using reduced data dimension. These two techniques were applied to land cover information extraction for Shanghai city using a HJ-1 CCD image. Results indicate that the information extraction accuracy of the object-oriented method was much higher than that of the pixel-based method. 相似文献
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基于面向对象的无监督分类的遥感影像自动分类方法 总被引：3，自引：0，他引：3
为了实现无任何先验知识的高分辨率遥感数据的自动分类,并进一步提高自动分类精度和效率,提出了一种基于面向对象的无监督分类方法(Object Oriented Unsupervised Classification).具体步骤如下:首先对遥感影像进行分割,得到一系列空间上相邻、同质性较好的分割单元,然后对分割单元进行特征提取,得到分割单元的对象特征(光谱特征、纹理特征等多特征信息),进而对分割单元进行基于对象特征马氏距离聚类.最后,通过分类后处理(类别合并、错分类别调整等)得到最终的分类结果.通过实验表明:本文提出的方法不仅能够利用影像中更多的特征信息进行聚类而且还可以有效地减少聚类对象的个数,从而使自动分类的精度和效率都得到较大的提升. 相似文献
7.
Image classification approaches are widely used in mapping vegetation on remotely sensed images. Vegetation assemblages are equivalent to habitats. Whereas sub-pixel classification approaches potentially can produce more realistic, homogenous habitat maps, pixel-based hard classifier approaches often result in non-homogenous habitat zones. This salt-and-pepper habitat mapping is particularly a challenge on images of savannas, given the characteristic patchy texture of scattered trees and grass. Image segmentation techniques offer possibilities for homogenous habitat classification. This study aimed at establishing the extent to which established, field surveyed and geology-related vegetation types in South Africa’s Kruger National Park (KNP) can be reproduced using image segmentation. Rain season Landsat TM images were used, selected to coincide with the peak in vegetation productivity, which was deemed the time of year when discrimination between key habitats in KNP is most likely to be successful. The multiresolution segmentation mode in eCognition 5.0 was employed, object classification accomplished using the nearest neighbour (NN) classifier, using object texture and training area mean values in the NN feature space.Compared to delineations of the vegetation types of KNP on a digital map of the vegetation zones that was tested, image segmentation successfully mapped the zones (overall accuracy 85.3%, K^ = 82.7%) despite slight shifts in the location of vegetation zone boundaries. Maximum likelihood classification (MLC) of the same images was only 37% accurate (K^ = 24.2%). Whereas the vegetation zones resulting from MLC were non-homogenous, with considerable spectral confusion among the vegetation zones, image segmentation produced more homogenous vegetation zones, comparably more useful for conservation management, because realistic and meaningful habitat maps are important in biodiversity conservation as input data upon which to base management decisions. Image segmentation appears to be a useful approach in mapping savanna vegetation. 相似文献
8.
Canonical discriminant function analysis was employed to discriminate between electron microprobe-determined titanomagnetite
and hornblende analyses from Egmont volcano and Tongariro Volcanic Centre. Data sets of 436 titanomagnetite and 206 hornblende
analyses from the two sources were used for the study. Titanomagnetite chemistry provided the best discrimination between
these two sources with classification efficiencies of 99% for sample averages and 95% for individual analyses. The difference
between sources for hornblende chemistry was less marked, but classification efficiencies of 100% for sample averages and
87% for individual analyses were achieved. Using the same methods a preliminary discrimination of individual Egmont volcano-sourced
tephras was attempted. Titanomagnetite chemistry enabled the discrimination of several individual tephras or at least pairs
of tephra units, but hornblende chemistry provided little discrimination. This technique provides an improvement on previous
methods for chemically distinguishing distal tephra from the two sources as well as potentially identifying individual tephras
from a particular source. A major advantage over previous discrimination techniques is that individual analyses can be classified
with a known probability of group membership (with groups such as volcano source or an individual tephra unit). Tephras in
a depositional environment where mixing is common such as within soil, loess and marine sequences, can be sourced or identified
more easily with classification of individual grains.
Received: 19 July 1995 / Accepted: 13 February 1996 相似文献
9.
STUDY ON THE MRF-BASED METHOD FOR DAMAGED BUILDINGS EXTRACTION FROM THE SINGLE-PHASE SEISMIC IMAGE 
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下载免费PDF全文 Earthquake events are one of the most extraordinarily serious natural calamities, which not only cause heavy casualties and economic losses, but also various secondary disasters. Such events are devastating, and have far-reaching influences. As the main disaster bearing body in earthquake, buildings are often seriously damaged, thus it can be used as an important reference for earthquake damage assessment. Identifying damaged buildings from post-earthquake images quickly and accurately is of real importance, which has guidance meaning to rescue and emergency response. At present, the assessment of earthquake damage is mainly through artificial field investigation, which is time-consuming and cannot meet the urgent requirements of rapid emergency response. Markov Random Field(MRF)combines the neighborhood system of pixels with the prior distribution model to effectively describe the dependence between spatial pixels and pixels, so as to obtain more accurate segmentation results. The support vector machine(SVM)model is a simple and clear mathematical model which has a solid theoretical basis; in addition, it also has unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition problems. Thus, in this paper, a Markov random field-based method for damaged buildings extraction from the single-phase seismic image is proposed. The framework of the proposed method has three components. Firstly, Markov Random Field was used to segment the image; then, the spectral and texture features of the post-earthquake damaged building area are extracted. After that, Support Vector Machine was used to extract the damaged buildings according to the extracted features. In order to evaluate the proposed method, 5 areas in ADS40 earthquake remote sensing image were selected as experimental data, this image covers parts of Wenchuan City, Sichuan Province, where an earthquake had struck in 2008. And in order to verify the applicability of this method to different resolution images, an experimental area was selected from different resolution images obtained by the same equipment. The experimental results show that the proposed method has good performance and could effectively identify the damaged buildings after the earthquake. The average overall accuracy of the selected experimental areas is 93.02%. Compared with the result extracted by the widely used eCognition software, the proposed method is simpler in operation and can improve the extraction accuracy and running time significantly. Therefore, it has significant meaning for both emergency rescue work and accurate disaster information providing after earthquake. 相似文献
10.
由于工业CT图像结构复杂,存在各种伪影,并且灰度分布呈现区域性质等特点,难以准确找出分割阈值,为此提出了一种适用于工业CT图像的分割算法。首先利用最大类间方差法和图像处理方法处理了外层伪影,然后利用聚类迭代的方法处理中心空气,得到感兴趣的区域。实验结果表明,此算法能够对具有先验知识的工业断层图像准确地提取感兴趣的区域。 相似文献
11.
为解决与光学遥感图像不同的合成孔径雷达(SAR)图像中存在大量混合像元的问题,本文提出了一种基于拉格朗日分解算法的SAR图像混合像元分解的方法,结合相关内容中具体定理的证明,文中给出拉格朗日分解算法用于SAR图像混合像元分解的系统的求解方法.用人工模拟SAR图像和ENVISAT SAR图像进行实验,结果表明拉格朗日分解算法的混合像元分解结果明显优于非约束类神经网络(文中实验以BP神经网络为例)的分解结果. 相似文献
12.
近年来,单谱与多谱磁共振图像的分割方法研究已经取得了很大进展,并应用于正常年龄的脑发育和脑疾病的诊断研究,例如老年痴呆综合征、脑损伤、脑肿瘤等的临床研究等。据此可以通过多谱MR图像获得多种对比度信息,更加准确表达人体组织及其病理情况。现已提出的大多数方法,把组织分割问题考虑成统计决策、模式识别和聚类、图像处理和分析等问题。在这些方法中用于组织分割的特征主要是单谱/多谱图像的灰度值/矢量,它们不能直接反映组织的物理特征。而且,这些方法将组织分割问题表述成组织磁共振图像中像素不同成分的有限集合。所以,这些组织分割的方法所取得的结果在某种意义上是不够合理的。这篇论文提出了一种基于磁共振图像谱分解的新的组织分割方法,该方法将组织分割问题考虑成组织的磁共振物理参数的估计问题。这个方法不仅用于磁共振成像中脂肪信号的抑止,也用于核磁共振谱中的水信号抑止。因此,这个方法可以称为空间和谱MR成像。 相似文献
13.
Within areas of salt tectonics, seismic imaging requires extensive updating of the velocity model. This includes defining the boundaries of salt structures which are often characterized by changes in texture of the seismic signal rather than reflectivity. The main characteristics of texture inside salt structures are identified. Three groups of texture attributes are studied: gray-level co-occurrence matrix (GLCM) attributes, frequency-based attributes, and dip and similarity attributes. Various combinations of the selected attributes are tested in a supervised Bayesian classification method. Experimental results show that the classification performance improves by combining at least two texture attribute groups. The classifier computes an estimate of the pixelwise probability of salt. It can then be applied to compute the probability of salt on different seismic sections. Classification results were found more robust based on timeslices. The result from classification, the salt probability image, is then input to a segmentation algorithm that produces a smooth border delimiting the extent of the salt. The segmented salt contours corresponded fairly well to the contours provided by an interpreter. 相似文献
14.
极光是太阳风能量注入到极区的指示器,从观测视野中准确分割出极光区域对研究极光演变如亚暴过程有非常重要的意义.本文基于全卷积神经网络提出了一种弱监督极光图像自动分割策略,数据标记时仅需指定极光区域的一个像素点即可,极大解决了机器学习人工标注数据的压力.首先利用简单单弧状极光图像训练一个初始分割模型Model 1,然后基于该模型,结合热点状和复杂多弧状极光图像获得一个增强的分割模型Model 2,最后对分割结果做进一步优化.本文对2003—2007年北极黄河站越冬观测的2715幅极光图像进行了分割,并和最新论文结果及人工标签进行了定量和定性比较,其中分割结果与人工标签的“交并比”高达60%,证明了本文方法的有效性. 相似文献
15.
角反射器精确定位是角反射器微形变获取的前提,文中从研究角反射器影像特征入手,详细介绍了角反射器强度、相关性和光谱离散指数（MSR-Mean to Standard deviation Ratio）影像特征：1）角反射器强度影像特征明显,其影像扩散在多个像素上,Envisat数据中角反射器表现为亮点,而TerraSAR数据中角反射器呈明显的十字型;2）角反射器MSR影像特征不明显;3）不同带宽多视处理后统计得到的角反射器相关性聚焦在一个像素内,而且相关值明显高于周边像素。将角反射器强度、相关性和MSR作为衡量光谱稳定性的参数,对比分析不同参数定位出的角反射器像素位置及其对应的相位信息,结果表明：角反射器强度易受背景干扰,此参数定位角反射器容易产生偏差;角反射器MSR值与背景差别小,不能单独用于角反射器定位;角反射器相关性聚焦性好,相关性参数能准确定位角反射器的像素位置,对应的相位值比其他参数定位得到的相位值相对稳定。 相似文献
16.
遥感分类方法在建筑物震害提取中的应用（以玉树地震为例） 总被引：1，自引：0，他引：1
建筑物损毁情况是地震灾害评估的一项重要指标,利用遥感技术快速提取震后建筑物震害信息,对科学指导地震应急救援工作具有重要意义.利用2010年4月14日青海玉树7.1级地震前后玉树县结古镇团结村高分辨率遥感影像,结合像素光谱和空间特性的纹理、结构等多源信息,基于支持向量机（SVM）方法,对地震前后建筑物信息进行分类提取,变化检测出建筑物损毁情况,并与面向对象多源信息复合的模糊分类法的分类精度、提取效率进行对比分析.研究结果表明,多源数据复合的SVM影像分类方法能够有效解决模糊分类影像破碎问题,地震前后两实相影像分类总精度达到77.53％和73.56％,提高了建筑物震害信息提取精度. 相似文献
17.
Airborne scanning laser altimetry (LiDAR) is an important new data source that can provide two‐dimensional river flood models with spatially distributed floodplain topography for model bathymetry, together with vegetation heights for parameterization of model friction. Methods are described for improving such models by decomposing the model's finite‐element mesh to reflect floodplain vegetation features such as hedges and trees having different frictional properties to their surroundings, and significant floodplain topographic features having high height curvatures. The decomposition is achieved using an image segmentation system that converts the LiDAR height image into separate images of surface topography and vegetation height at each point. The vegetation height map is used to estimate a friction factor at each mesh node. The spatially distributed friction model has the advantage that it is physically based, and removes the need for a model calibration exercise in which free parameters specifying friction in the channel and floodplain are adjusted to achieve best fit between modelled and observed flood extents. The scheme was tested in a modelling study of a flood that occurred on the River Severn, UK, in 1998. A satellite synthetic aperture radar image of flood extent was used to validate the model predictions. The simulated hydraulics using the decomposed mesh gave a better representation of the observed flood extent than the more simplistic but computationally efficient approach of sampling topography and vegetation friction factors on to larger floodplain elements in an undecomposed mesh, as well as the traditional approach using no LiDAR‐derived data but simply using a constant floodplain friction factor. Use of the decomposed mesh also allowed velocity variations to be predicted in the neighbourhood of vegetation features such as hedges. These variations could be of use in predicting localized erosion and deposition patterns that might result in the event of a flood. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
18.
Anglique Berthelot Anne H. S. Solberg Erlend Morisbak Leiv‐J Gelius 《Geophysical Prospecting》2011,59(4):682-696
Prestack depth imaging of seismic data in complex areas such as salt structures requires extensive velocity model updating. In many cases, salt boundaries can be difficult to identify due to lack of seismic reflectivity. Traditional amplitude based segmentation methods do not properly tackle this problem, resulting in extensive manual editing. This paper presents a selection of seismic attributes that can reveal texture differences between the salt diapirs and the surrounding geology as opposed to amplitude‐sensitive attributes that are used in case of well defined boundaries. The approach consists of first extracting selected texture attributes, then using these attributes to train a classifier to estimate the probability that each pixel in the data set belongs to one of the following classes: near‐horizontal layering, highly‐dipping areas and the inside of the salt that appears more like a low amplitude area with small variations in texture. To find the border between the inside of the salt and the highly‐dipping surroundings, the posterior probability of the class salt is input to a graph‐cut algorithm that produces a smooth, continuous border. An in‐line seismic section and a timeslice from a 3D North Sea data set were employed to test the proposed approach. Comparisons between the automatically segmented salt contours and the corresponding contours as provided by an experienced interpreter showed a high degree of similarity. 相似文献
19.
It is well known that there is a degree of fuzzy uncertainty in land cover classification using remote sensing (RS) images. In this article, we propose a novel fuzzy uncertainty modeling algorithm for representing the features of land cover patterns, and present an adaptive interval type-2 fuzzy clustering method. The proposed fuzzy uncertainty modeling method is performed in two main phases. First, the segmentation units of the input multi-spectral RS image data are subjected to objectbased interval-valued symbolic modeling. As a result, features for each land cover type are represented in the form of an intervalvalued symbolic vector, which describes the intra-class uncertainty better than the source data and improves the separability between different classes. Second, interval type-2 fuzzy sets are generated for each cluster based on the distance metric of the interval-valued vectors. This step characterizes the inter-class high-order fuzzy uncertainty and improves the classification accuracy. To demonstrate the advantages and effectiveness of the proposed approach, extensive experiments are conducted on two multispectral RS image datasets from regions with complex land cover characteristics, and the results are compared with those given by well-known fuzzy and conventional clustering algorithms. 相似文献