Integration of spatial attractions between and within pixels for sub-pixel mapping

As a promising technique to enhance the spatial reso- lution of remote sensing imagery, sub-pixel mapping is processed based on the spatial dependence theory with the assumption that the land cover is spatially dependent both within pixels and be- tween them. The spatial attraction is used as a tool to describe the dependence. First, the spatial attractions between pixels, sub- pixel/pixel spatial attraction model （SPSAM）, are described by the modified SPSAM （MSPSAM） that estimates the attractions accord- ing to the distribution of sub-pixels within neighboring pixels. Then a mixed spatial attraction model （MSAM） for sub-pixel mapping is proposed that integrates the spatial attractions both within pix- els and between them. According to the expression of the MSAM maximumising the spatial attraction, the genetic algorithm is em- ployed to search the optimum solution and generate the sub-pixel mapping results. Experiments show that compared with SPSAM, MSPSAM and pixel swapping algorithm modified by initialization from SPSAM （MPS）, MSAM can provide higher accuracy and more rational sub-pixel mapping results.

Sub-pixel high precision dimensional measurement method for aero-engine hollow turbine blade based on industrial computed tomography image

Aero-engine hollow turbine blades are work under prolonged high temperature,requiring high dimensional accuracy.Blade profile and wall thickness are important parameters to ensure the comprehensive performance of blades,which need to be measured accurately during manufacturing process.In this study,a high accuracy industrial computed tomography(ICT)measuring method was developed based on standard cylindrical pin and ring workpieces of different sizes.Combining ICT with cubic spline interpolation,a sub-pixel accuracy was achieved in measuring the dimension of component.Compared with the traditional and whole-pixel level image measurement method,the cubic spline interpolation algorithm has the advantages of high accuracy in image edge detection and thus high accuracy of dimensional measurement.Further,the technique was employed to measure the profile and wall thickness of a typical aerospace engine turbine blade,and an accuracy higher than 0.015 mm was obtained.

Fast Approaches for Sub-pixel Precision Variable Block Size Motion Estimation

The new features of H. 264 video coding standard make the motion estimation module much more time consuming than before. Especially, the motion search is required for each of the 4 modes for inter prediction. In order to reduce the computational complexity, we analyze the statistics of results of motion estimation, such as the continuity of best modes of blocks in successive frames and the chance to give up a sub-partition mode （smaller than 16 × 16） after integer-pixel motion estimation, from which we suggest to make mode prediction based on the motion information of the previous frame and skip sub-pixel motion estimation in subpartition mode selectively. According to the experimental result, the proposed algorithm can save 75 % of the computational time with a slight degradation （0.03 dB） on PSNR compared with the pseudocode of fast search motion estimation in JM12.2.

Integrating CART Algorithm and Multi-source Remote Sensing Data to Estimate Sub-pixel Impervious Surface Coverage:A Case Study from Beijing Municipality,China

The sub-pixel impervious surface percentage(SPIS) is the fraction of impervious surface area in one pixel,and it is an important indicator of urbanization.Using remote sensing data,the spatial distribution of SPIS values over large areas can be extracted,and these data are significant for studies of urban climate,environment and hydrology.To develop a stabilized,multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons,an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree(CART) algorithm.First,models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data.The optimal model was selected through the analysis and comparison of the assessed accuracy of these models.Subsequently,multi-temporal SPIS mapping was carried out based on the optimal model.The results are as follows:1) multi-seasonal images and nighttime light(NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality,where the intra-annual variability in vegetation is distinct.The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively.NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces.After testing,the SPIS modeling correlation coefficient(r) is approximately 0.86,the average error(AE) is approximately 12.8%,and the relative error(RE) is approximately 0.39.2) The SPIS results have been divided into areas with high-density impervious cover(70%–100%),medium-density impervious cover(40%–70%),low-density impervious cover(10%–40%) and natural cover(0%–10%).The SPIS model performed better in estimating values for high-density urban areas than other categories.3) Multi-temporal SPIS mapping(1991–2016) was conducted based on the optimized SPIS results for 2005.After testing,AE ranges from 12.7% to 15.2%,RE ranges from 0.39 to 0.46,and r ranges from 0.81 to 0.86.It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.

Sub-pixel mapping method based on BP neural network

A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information.Then the sub-pixel scaled target could be predicted by the trained model.In order to improve the performance of BP network,BP learning algorithm with momentum was employed.The experiments were conducted both on synthetic images and on hyperspectral imagery(HSI).The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.

Fast Sub-Pixel Motion Estimation Algorithm For H.264

A novel fast sub-pixel search algorithm is proposed to accelerate sub-pixel search. Based on the features of predicted motion vector (PMV) and texture direction observed, the proposed method effectively filters out impossible points and thus decreases 11 searched points in average during the sub-pixel search stage. A threshold is also adopted to early terminate the sub-pixel search. Simulation results show that the proposed method can achieve up to 4.8 times faster than full sub-pixel motion search scheme (FSPS) with less than 0.025 dB PSNR losses and 2.2% bit-length increases.

基于机器视觉的木窗双端铣削加工尺寸测量方法

木窗是一种以木材或木质复合材料为主要构件的门窗产品,具有良好的生态性能和美观效果,适用于多种建筑形式和风格,其中木窗尺寸是衡量木窗加工是否合格的重要指标。对于传统木窗双端铣削加工中人工测量尺寸方式存在的精度低、效率低等问题,提出一种基于机器视觉的木窗双端铣削加工尺寸测量方法,以期提高尺寸测量精度及加工效率。该方法针对木窗厚度引起的透视效应,提出一种物平面提升法,以消除透视投影带来的误差。首先对木窗图像采取灰度化、平滑去噪、图像增强及轮廓分割等举措,完成图像预处理,提取出木窗内外轮廓区域。对轮廓区域应用Canny算子获取木窗像素级轮廓。通过优化的Zernike矩亚像素边缘提取算法对木窗像素级边缘进行更精确的定位,得到亚像素级轮廓坐标。通过最小二乘法联合RANSAC算法对亚像素轮廓坐标进行拟合,得到拟合轮廓及角点坐标,并使用透视矫正模型计算出木窗尺寸。实验利用3种厚度规格相同但尺寸不同的松木材质矩形木窗,分别测量其内框和外框的边框尺寸及对角线尺寸,并与对应的实际物理尺寸对比,验证了所提木窗尺寸测量方法的检测精度。研究结果表明,所提方法与实际物理尺寸值相比,其绝对误差范围在±0.12 mm之内,相对误差在±0.1%之内,且效率及精度高,可以满足对木窗的在线尺寸检测。

基于机器视觉的电芯绝缘介质定位算法

为保证电芯绝缘介质定位的准确性和时效性,文章提出一种基于双线性插值亚像素坐标结合改进随机抽样一致性(random sample consensus,RANSAC)算法的绝缘介质定位算法。对工业采集的图像进行空间滤波、阈值分割等预处理操作,分割出目标并增强特征;运用Canny边缘检测算子检测边缘,选取定位轮廓并根据最小外接矩形分离4条边缘直线,用双线性插值公式精确定位边缘直线亚像素坐标;采用部分点先验模型约束RANSAC算法提取4条边缘直线的高质量内点,再用最小二乘法分别拟合4条边缘直线并计算出相应偏移量。实验结果表明,该算法能有效保证绝缘介质的定位精度且具有一定的时效性,可以较好地满足实际生产应用的要求。

基于亚像素精度的汽车冲压件尺寸测量系统设计

针对汽车冲压件尺寸人工测量方式存在的检测效率低和测量精度难以保证等问题,提出了一种基于亚像素精度的尺寸测量方法。先对汽车冲压件进行图像采集和预处理,接着利用Canny算子和改进Zernike矩进行冲压件图像亚像素边缘提取,再通过Hough变换对提取的圆轮廓进行拟合,最终获得了冲压件外圆直径和3个内孔直径的精确尺寸,4个圆轮廓的测量精度均高于0.025 mm,且多次测量结果的标准差小于0.009 6 mm。基于亚像素精度的尺寸测量系统,完成了测量平台的搭建和软件系统的开发,实现了汽车冲压件尺寸的非接触式亚像素级精度测量,能够满足工业应用的快速连续性测量要求。

共形天线表面微小焊盘视觉定位算法设计

共形天线是一种特殊形态的新型雷达天线,具有低剖面性以及低散射面积,是未来雷达天线发展的重要趋势之一。当前,共形天线的装配方式正由人工装配转向自动化装配以提升生产效率和批产一致性,但共形天线表面的焊盘尺寸小,数量多且分布在复杂曲面上,现有视觉定位算法难以满足实际使用需求。为此,文中提出了一种多目标亚像素定位算法。该算法结合了模板匹配和亚像素定位算法,可以同时给出多个焊盘的精准位置信息。由仿真分析与实验验证可知,该算法的视觉定位精度可达5μm,有效提升了共形天线的装配质量。

基于亚像素定位的图像边缘检测策略研究

针对图像处理与计算机视觉技术中低对比度、边缘模糊图像的边缘检测问题,参考局部极值与梯度方向两种因素,并结合图像边缘方向趋势,提出了一种单像素边缘跟踪策略。相较于应用广泛的Canny算法,该跟踪策略无需设置全局阈值,实现方式更为简洁、高效;提取的图像边缘连续、平滑、完整,并有效地减少了图像边缘的冗余像素,进而提升了图像后续处理的效率;边缘跟踪方向抗干扰性强,具有较强的鲁棒性。为了减小检测的图像边缘与真实图像边缘之间的偏差、提高图像边缘检测的精度,参考边缘像素点的相邻区域灰度,以边缘像素点的梯度分布为依据对该像素点进行亚像素定位。经实验验证,经过亚像素优化的图像边缘检测策略可用于检测边缘模糊、对比度低的图像,检测的图像边缘完整、连续且平滑。该策略有效地消除了程序运算中引入的截断误差,提升了图像边缘检测精度,且适用于亮度5~100000 lx的高动态成像场景中。

一种基于加权拟合的定制家居封边板材尺寸测量方法

板材在流程化加工过程中,会因为辊道的不连续性导致其在运动过程中震动和倾斜,给其尺寸的在线测量带来困难。根据板材的形状特点,提出一种基于加权灰度矩的亚像素边界拟合定位方法,以实现宽度的准确测量。首先利用方向滤波和边缘算子进行边缘筛选,利用最小二乘法拟合边缘点集合得到像素级边缘,再基于离群度的灰度矩加权拟合方法,定位得出板材边缘的亚像素位置,克服了震动和光照造成的边缘离散问题,同时结合线阵相机的单维度标定方法,获得了板材宽度的测量结果。现场测试表明,该方法的测量精度小于0.1 mm,满足工厂测量的要求,且具有较好的鲁棒性。

基于图像处理的八角光纤尺寸检测算法设计

为解决传统的光纤端面尺寸检测方法中没有八角光纤,测量效率低的问题,采用图像处理对八角光纤尺寸检测进行算法设计,针对3个待测参数即纤芯直径、包层对边距离、包层对角距离,采用高斯滤波对图像进行降噪,提取待测光纤不同区域亚像素轮廓,对包层轮廓采用Ramer算法分割八边形光纤轮廓,提出过分割处理算法对不同长度的多段轮廓边进行处理,得到有效的八段轮廓;采用最小二乘法进行曲线拟合,纤芯轮廓拟合为圆测得其直径,包层轮廓各边拟合为直线测量对边距离;提出基于辅助线的顶点检测算法,进而测量对角距离。实验结果显示,本算法能对八角光纤纤芯直径、四组对边距离、四组对角距离进行精确快速测量,对边,对角距离重复测量精度平均达0.1μm,达到企业技术指标要求。

考虑点扩散函数效应的光谱遥感图像超分辨率制图

由于硬件设备的限制和土地覆盖类别的多样性,采集得到的光谱遥感图像的空间分辨率有时较为粗糙,导致大量混合像元产生,严重影响了土地覆盖类型空间分布的制图精度。超分辨率制图技术可以有效地处理光谱遥感图像中的混合像元,获得准确的地物类别分布信息。在遥感大数据背景下,来自同一卫星采集同一区域的多位移图像可以作为辅助数据改进超分辨率制图结果。然而,目前多位移图像插值超分辨率制图方法很少有效地考虑点扩散函数效应影响,导致制图结果精度降低。为了解决这一问题,本文提出了一种考虑点扩散函数效应的多位移光谱遥感图像超分辨率制图方法,改善土地覆盖类别制图结果。在所提出的方法中,首先,对多位移图像进行光谱解混,以生成粗糙丰度图像。然后,在考虑点扩散函数效应的情况下,对粗糙丰度图像先采用面到点克里格法,然后进行理想方波滤波,得到改善的粗糙丰度图像。接下来,通过插值对改善的粗糙丰度图像进行上采样以获得上采样丰度图像,并对上采样的所有丰度图像进行整合以获得精细丰度图像。最后,根据精细丰度图像提供的类别比例信息,使用类别分配方法将类别标签分配给亚像元,以获得理想的制图结果。实验结果表明,通过减少点扩散函数效应影响,所提出的方法展示出最佳性能表现,例如在美国华盛顿特区数据集实验结果的总体精度分别达到77.63%和Kappa系数达到0.7263。

基于亚像素边缘搜索的气门几何参数检测

气门几何参数检测问题是汽车、船舶、航空以及柴油发动机等工业缺陷检测领域的热点难题,其本质是气门边缘关键点的检测问题,目前应用在工业气门产业的成果较少。本文针对气门边缘关键点难以精准检测问题,设计了基于亚像素边缘搜索算法,该算法基于Zernike矩和亚像素边缘检测对气门边缘进行矫正,并结合图像投影和逐像素边缘搜索精确定位边缘关键点对其拟合,得到气门几何参数检测结果。为了验证所提算法的有效性,对不同尺寸的工件进行检测分析,结果表明该算法检测气门杆径的最大绝对值误差和最大极差分别低于0.0093 mm和0.0363 mm,在检测精度和稳定性上均达到了工业要求,满足实际工业生产现场的检测需求。

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral mixture.Especially for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution.Thus,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change detection.In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion.Nonlinear spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences.The proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas.The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(i.e.change vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.

Multi-resolution integration of land cover for sub-pixel estimation of urban impervious surface and forest cover

A methodology is presented for estimating percent coverage of impervious surface(IS)and forest cover(FC)within Landsat thematic mapper(TM)pixels of urban areas.High-resolution multi-spectral images from Quickbird(QB)play a key role in the sub-pixel mapping process by providing information on the spatial distributions of ISs and FCs at 2.4 m ground sampling intervals.Thematic classifications,also derived from the Landsat imagery,have then been employed to define relationships between 30 m Landsat-derived greenness values and percent IS and FC.By also utilizing land cover/land use classification derived from Landsat and defining unique relationships for urban sub-classes(i.e.residential,commercial/industrial,open land),confusion between impervious and fallow agricultural lands has been overcome.Test results are presented for Ottawa-Gatineau,an urban area that encompasses many aspects typical of the North American urban landscape.Multiple QB scenes have been acquired for this urban centre,thereby allowing us to undertake an in-depth study of the error budgets associated with the fractional inference process.

Sub-pixel analysis to enhance the accuracy of evapotranspiration determined using MODIS images

A study was carried out to estimate the actual evapotranspiration(ET)over a 1074 km2 of the humid area of Perak State(Malaysia),where water and evaporation cycle deeply influences the climate,natural resources and human living aspects.Images from both Terra and Aqua platforms of the Moderate Resolution Imaging Spectroradiometer(MODIS)sensor were used for ET estimation by employing the Surface Energy Balance Algorithm for Land(SEBAL)model.As a part of the accuracy assessment process,in-situ measurements on soil temperature and reference ET(ET0)were recorded at the time of satellite overpass.In order to enhance the accuracy of the generated ET maps,MODIS images were subjected to sub-pixel analysis by assigning weights for different land surface cover(urban,agriculture and multi-surface areas)reflections.The weighting process was achieved by integrating ET from pure pixels with the respective site-specific ET0 of each land cover.The enhanced SEBAL model estimated ET exhibited a good correlation with the in-situ measured Penman-Montieth ET0,with R2 values for the Aqua and the Terra platforms of 0.67 and 0.73,respectively.However,the correlation of the non-enhanced ET maps resulted in R2 values of 0.61 and 0.68 for the Aqua and the Terra platforms,respectively.Hence,the results of this study revealed the feasibility of employing the sub-pixel analysis method for an accurate estimation of ET over large areas.

Image improvement based on sub-pixel post-integration for a staring imaging system in geostationary orbit

This paper proposes the post-integration technology based on sub-pixel image registration and image fusion to improve the signal-to-noise ratio （SNR） of remote sensing images without motion degradation caused by satellite vibration. A two-dimensional vibration system is set up to simulate satellite disturbance. Image sequences with different exposure times are captured using a high-speed CMOS camera. The displacement plots are compared with the motion data measured by the grating linear encoder. These plots indicate that the accuracy of the registration algorithm is better than 0.1 pixels. The sub-pixel image fusion shows an improvement in image quality, thus indicating that this technology is powerful for staring imaging systems in geostationary orbit.

基于视觉原理的链轮尺寸测量方法研究

针对现有的链轮端面几何参数测量方法存在效率及测量精度低等问题,提出一种基于视觉原理的链轮端面几何参数测量方法。首先,利用Zernike矩的亚像素边缘检测提取链轮内外轮廓,对内轮廓采用Hough变换获得中心点坐标与中心孔半径;其次,在外轮廓作一辅助圆,通过像素点序列计数算法得到齿数;然后,根据链轮齿廓的形状特征,提出一种改进的局部半径域算法提取每段齿廓的齿顶、齿根点,再通过最小二乘法拟合像素点得到齿顶圆、齿根圆直径;最后,采用最小二乘法拟合链轮键槽边缘直线得到键槽尺寸。对2种不同规格的链轮分别进行10次重复性测量。实验结果表明,链轮端面几何参数的测量误差均小于40μm,相对误差均在0.4%之内,能满足高精度的自动测量需求。