共查询到20条相似文献，搜索用时 15 毫秒
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AbstractA super-resolution enhancement algorithm was proposed based on the combination of fractional calculus and Projection onto Convex Sets (POCS) for unmanned aerial vehicles (UAVs) images. The representative problems of UAV images including motion blur, fisheye effect distortion, overexposed, and so on can be improved by the proposed algorithm. The fractional calculus operator is used to enhance the high-resolution and low-resolution reference frames for POCS. The affine transformation parameters between low-resolution images and reference frame are calculated by Scale Invariant Feature Transform (SIFT) for matching. The point spread function of POCS is simulated by a fractional integral filter instead of Gaussian filter for more clarity of texture and detail. The objective indices and subjective effect are compared between the proposed and other methods. The experimental results indicate that the proposed method outperforms other algorithms in most cases, especially in the structure and detail clarity of the reconstructed images. 相似文献
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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
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针对大规模无序影像稀疏三维重建问题,本文提出一种稳健、高效且易于并行的分区优化的混合式SfM方法。首先,利用SIFT算法进行影像匹配,无须GPS/INS等其他辅助信息,仅利用影像间的匹配结果计算得到的影像关联度完成影像分区。然后,提出一种改进的增量式SfM方法实现每个分区内快速重建,以及提出多项标准自动剔除不可靠分区并将这些分区内影像重新划分至其他分区,实现分区的动态调整。最后,提出一种稳健高精度的分区融合算法,实现相机参数、影像姿态和场景三维信息的准确融合。多组不同规模、不同影像类型以及不同场景的典型数据试验结果表明本文方法对不同数据集具有很好的稳健性,在保持高精度的同时能大大提高重建效率,尤其适用于大规模影像数据集。 相似文献
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AbstractThis work presents a mapping and tracking system based on images to enable a small Unmanned Aerial Vehicle (UAV) to accurately navigate in indoor and GPS-denied outdoor environments. A method is proposed to estimate the UAV’s pose (i.e., the 3D position and orientation of the camera sensor) in real-time using only the on-board RGB camera as the UAV travels through a known 3D environment (i.e., a 3D CAD model). Linear features are extracted and automatically matched between images collected by the UAV’s onboard RGB camera and the 3D object model. The matched lines from the 3D model serve as ground control to estimate the camera pose in real-time via line-based space resection. The results demonstrate that the proposed model-based pose estimation algorithm provides sub-meter positioning accuracies in both indoor and outdoor environments. It is also that shown the proposed method can provide sparse updates to correct the drift from complementary simultaneous localization and mapping (SLAM)-derived pose estimates. 相似文献
5.
In recent years, it has been widely agreed that spatial features derived from textural, structural, and object-based methods are important information sources to complement spectral properties for accurate urban classification of high-resolution imagery. However, the spatial features always refer to a series of parameters, such as scales, directions, and statistical measures, leading to high-dimensional feature space. The high-dimensional space is almost impractical to deal with considering the huge storage and computational cost while processing high-resolution images. To this aim, we propose a novel multi-index learning (MIL) method, where a set of low-dimensional information indices is used to represent the complex geospatial scenes in high-resolution images. Specifically, two categories of indices are proposed in the study: (1) Primitive indices (PI): High-resolution urban scenes are represented using a group of primitives (e.g., building/shadow/vegetation) that are calculated automatically and rapidly; (2) Variation indices (VI): A couple of spectral and spatial variation indices are proposed based on the 3D wavelet transformation in order to describe the local variation in the joint spectral-spatial domains. In this way, urban landscapes can be decomposed into a set of low-dimensional and semantic indices replacing the high-dimensional but low-level features (e.g., textures). The information indices are then learned via the multi-kernel support vector machines. The proposed MIL method is evaluated using various high-resolution images including GeoEye-1, QuickBird, WorldView-2, and ZY-3, as well as an elaborate comparison to the state-of-the-art image classification algorithms such as object-based analysis, and spectral-spatial approaches based on textural and morphological features. It is revealed that the MIL method is able to achieve promising results with a low-dimensional feature space, and, provide a practical strategy for processing large-scale high-resolution images. 相似文献
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一种基于无人机位姿信息的航拍图像拼接方法 总被引：1，自引：0，他引：1
针对现有航拍图像拼接方法处理速度较慢的问题,提出一种基于无人机位姿信息的快速拼接方法。首先从机载GPS和惯性导航单元获得无人机航拍时的坐标和姿态角,根据每一幅航拍图像对应的无人机坐标和姿态角计算它们之间的单应变换矩阵,实现航拍图像之间的快速配准。然后通过单应变换矩阵的运算得到拼接图像之间的配准,最后完成多幅图像的拼接得到整个区域的全景图。试验结果证明该方法快速有效。 相似文献
7.
Özlem Akar 《国际地球制图》2018,33(5):538-553
This study aims to test the performance of the Rotation Forest (RTF) algorithm in urban and rural areas that have similar characteristics using unmanned aerial vehicle (UAV) images to produce the most up-to-date and accurate land use maps. The performance of the RTF algorithm was compared to other ensemble methods such as Random Forest (RF) and Gentle AdaBoost (GAB) for object-based classification. RGB bands and other variables (i.e. ratio, mean, standard deviation, ... etc.) were also used in classification. The accuracy assessments showed that the RTF method, with 92.52 and 91.29% accuracies, performed better than the RF (2 and 4%) and GAB (5 and 8%) methods in urban and rural areas, respectively. The significance of differences in classification methods was also analysed using the McNemar test. Consequently, this study shows the success of the RTF algorithm in the object-based classification of UAV images for land use mapping. 相似文献
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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
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综合多特征的极化SAR图像随机森林分类算法 总被引：2，自引：1，他引：1
为抑制相干斑噪声对极化SAR图像分类结果的干扰,本文提出一种综合多特征的极化SAR图像随机森林分类方法。该方法首先利用简单线性迭代聚类(SLIC)算法生成超像素作为分类单元;然后,基于高维极化特征图像,利用训练好的随机森林模型,统计决策树的分类投票数,计算各超像素的类别概率;最后,利用超像素间的空间邻域特征,采用概率松弛算法(PLR)迭代修正超像素的类别后验概率,并依据最大后验概率(MAP)准则得到分类结果;实现综合利用超像素和空间邻域特征,降低相干斑噪声干扰的极化SAR图像分类方法。实验对比结果表明:本文方法能得有效抑制极化SAR图像中相干斑噪声的干扰,得到高精度且光滑连续的分类结果。 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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AbstractMangrove ecosystems play a very important ecological role on land–ocean interfaces in tropical regions. These ecosystems comprise of various tree species and aquatic animals, protecting the environment and providing a habitat that supports many living organisms including humans. The identification of image regions in mangrove ecosystems plays a significant role in ecosystem monitoring and conservation. Recent studies have suggested oversegmentation of colour images using superpixels as a solution to the segmentation of image regions. This study used the SLIC superpixel algorithm and k-means clustering to segment images taken from a camera mounted on a drone from a mangrove ecosystem in Fiji. The SLIC superpixel algorithm performed well to demarcate image regions with similar colour and texture information into patches and to use k-means for the segmentation of the whole image. These results lend support to the use of superpixel algorithms for the segmentation of mangrove ecosystems. Understanding how superpixels can be used for the segmentation of drone images will assist conservation efforts in mangrove ecosystems. 相似文献
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《International Journal of Digital Earth》2013,6(7):701-718
ABSTRACTIn forest ecosystem studies, tree stem structure variables (SSVs) proved to be an essential kind of parameters, and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle. For this newly emerging task, satellite imagery such as WorldView-2 panchromatic images (WPIs) is used as a potential solution for co-prediction of tree-level multifarious SSVs, with static terrestrial laser scanning (TLS) assumed as a ‘bridge’. The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters, and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models (termed as Model1s and Model2s). In the case of Picea abies, Pinus sylvestris, Populus tremul and Quercus robur in a boreal forest, tests showed that Model1s and Model2s for different tree species can be derived (e.g. the maximum R2?=?0.574 for Q. robur). Overall, this study basically validated the algorithm proposed for co-prediction of multifarious SSVs, and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling, which is useful for large-scale investigations of forest understory, macroecosystem ecology, global vegetation dynamics and global carbon cycle. 相似文献
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多尺度分析技术广泛应用于高分辨率遥感影像的特征提取和建模。分解层数受制于影像的大小,下采样小波变换实现的影像多尺度表达难以描述大范围的空间模式,导致分类结果出现"胡椒盐"现象;面向对象的影像分析技术虽避免了"胡椒盐"现象,但由于仅利用了单尺度的的特征,也难以描述影像多层次的空间模式,导致分类精度较低。为改善分类结果中的"胡椒盐"现象和提高分类精度,提出了一种结合区域多尺度遥感影像分割和马尔可夫随机场的分类方法。首先,获得原始影像过分割区域,依据区域内亮度均值以及区域间的共享边界长度信息,提取影像低频和高频特征,采用该低频特征波段代替原始影像,重复分割与特征波段提取过程,形成影像的区域多尺度表达。然后,以原始图像为初始尺度,以分割区域为处理单元,以更细尺度分类结果为标记场先验,以当前高频特征建立特征场,逐层分类、投影,获得最终尺度分类结果。合成纹理影像和多光谱遥感影像的实验表明:相比于小波域多尺度建模方法和单尺度区域建模方法,本文提出的方法可以有效提高分类精度,并避免"胡椒盐"现象的产生。 相似文献
16.
Tengfei Su 《地理信息系统科学与遥感》2019,56(6):811-842
Image segmentation has a remarkable influence on the classification accuracy of object-based image analysis. Accordingly, how to raise the performance of remote sensing image segmentation is a key issue. However, this is challenging, primarily because it is difficult to avoid over-segmentation errors (OSE) and under-segmentation errors (USE). To solve this problem, this article presents a new segmentation technique by fusing a region merging method with an unsupervised segmentation evaluation technique called under- and over-segmentation aware (UOA), which is improved by using edge information. Edge information is also used to construct the merging criterion of the proposed approach. To validate the new segmentation scheme, five scenes of high resolution images acquired by Gaofen-2 and Ziyuan-3 multispectral sensors are chosen for the experiment. Quantitative evaluation metrics are employed in the experiment. Results indicate that the proposed algorithm obtains the lowest total error (TE) values for all test images (0.3791, 0.1434, 0.7601, 0.7569, 0.3169 for the first, second, third, fourth, fifth image, respectively; these values are averagely 0.1139 lower than the counterparts of the other methods), as compared to six state-of-the-art region merging-based segmentation approaches, including hybrid region merging, hierarchical segmentation, scale-variable region merging, size-constrained region merging with edge penalty, region merging guided by priority, and region merging combined with the original UOA. Moreover, the performance of the proposed method is better for artificial-object-dominant scenes than the ones mainly covering natural geo-objects. 相似文献
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基于Harris与RANSAC算法的无人机影像拼接方法研究 总被引：1，自引：0，他引：1
针对Harris算法在进行无人机影像拼接时的特征点误匹配问题,利用RANSAC算法对误匹配的特征点进行剔除,进而实现无人机影像的无缝拼接。首先,基于Harris算法提取兴趣点,利用最短欧式距离进行特征匹配;然后,利用RANSAC算法实现对特征点的精确匹配;最后,利用RANSAC算法得到的单应性矩阵完成无人机影像拼接。实验结果表明:本文方法能够较好地剔除无人机影像拼接时误匹配的特征点,实现对无人机影像的拼接,拼接效果良好。 相似文献
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《International Journal of Digital Earth》2013,6(11):1077-1097
ABSTRACTThis paper presents an approach to process raw unmanned aircraft vehicle (UAV) image-derived point clouds for automatically detecting, segmenting and regularizing buildings of complex urban landscapes. For regularizing, we mean the extraction of the building footprints with precise position and details. In the first step, vegetation points were extracted using a support vector machine (SVM) classifier based on vegetation indexes calculated from color information, then the traditional hierarchical stripping classification method was applied to classify and segment individual buildings. In the second step, we first determined the building boundary points with a modified convex hull algorithm. Then, we further segmented these points such that each point was assigned to a fitting line using a line growing algorithm. Then, two mutually perpendicular directions of each individual building were determined through a W-k-means clustering algorithm which used the slop information and principal direction constraints. Eventually, the building edges were regularized to form the final building footprints. Qualitative and quantitative measures were used to evaluate the performance of the proposed approach by comparing the digitized results from ortho images. 相似文献
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《International Journal of Digital Earth》2013,6(3):219-236
Abstract This paper investigates the contribution of multi-temporal enhanced vegetation index (EVI) data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer (MODIS) imagery. In object-oriented classification, similar pixels are firstly grouped together and then classified; the produced result does not suffer the speckled appearance and closer to human vision. EVI data are from the MODIS sensor aboard Terra spacecraft. 69 EVI data (scenes) were collected during the period of three years (2001–2003) in a mountainous vegetated area. These data sets were used to study the phenology of the land cover types. Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification. Two experiments were carried out: one was only with single date MODIS multispectral data, and the other one including also the 69 EVI images. Eight classes were distinguished: temperate forest, tropical dry forest, grassland, irrigated agriculture, rain-fed agriculture, orchards, lava flows and human settlement. The two classifications were evaluated with independent verification data, and the results showed that with multi-temporal EVI data, the classification accuracy was improved 5.2%. Evaluated by McNemar's test, this improved was significant, with significance level p=0.01. 相似文献
20.
Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images. 相似文献