基于各向异性Markov随机场的遥感影像亚像元尺度建筑物提取

基于遥感影像的建筑物自动提取方法容易受混合像元影响,目标提取精度不高。亚像元定位可以提取亚像元尺度地物分布信息,减轻混合像元对目标提取结果造成的影响。传统亚像元定位模型采用各向同性邻域描述地物的空间相关性,并没有考虑地物特有的形状信息,难以满足建筑物提取的需要。在考虑建筑物光谱特征的基础上,建立了平行与垂直于目标建筑物主方向的各向异性邻域,并采用基于各向异性Markov随机场的亚像元定位模型进行了亚像元尺度的建筑物提取。基于QuickBird多光谱数据与AVIRIS高光谱数据的实验结果表明,该模型提取的建筑物不仅具有更高的空间分辨率,而且能够较好地保持建筑物边缘与角点的形状信息,是一种有效的亚像元尺度建筑物提取方法。     

单星星图细分定位算法的研究

介绍了单星星图的特点，阐述了几种实用的提高星体定位精度的细分算法，并对这几种算法的精度进行了分析。将各种细分定位算法应用于模拟单星星图星体坐标的提取，对模拟星图中星体中心位于CCD像元之间任意位置的情况下，这些算法所能达到的精度作了详细的比较。仿真结果表明平方加权质心法和高斯曲面拟合法是比较好的星体细分定位算法，可依据应用的环境进行选择。     

基于目标优化的高光谱图像亚像元定位

目的 高光谱图像混合像元的普遍存在使得传统的分类技术难以准确确定地物空间分布,亚像元定位技术是解决该问题的有效手段。针对连通区域存在孤立点或孤立两点等特例时,通过链码长度求周长最小无法保证最优结果及优化过程计算量大的问题,提出了一种改进的高光谱图像亚像元定位方法。方法 以光谱解混结合二进制粒子群优化构建算法框架,根据光谱解混结果近似估计每个像元对应的亚像元组成,通过分析连通区域存在特例时基于链码长度求周长最小无法保证结果最优的原因,提出修改孤立区域的周长并考虑连通区域个数构造代价函数,最后利用二进制粒子群优化实现亚像元定位。为了减少算法的时间复杂度,根据地物空间分布特点,采用局部分析代替全局分析,提出了新的迭代优化策略。结果 相比直接基于链码长度求周长最小的优化结果,基于改进的目标函数优化后,大部分区域边界更明显,并且没有孤立1点和孤立两点的区域,识别率可以提高2%以上,Kappa系数增加0.05以上,新的优化策略可以使算法运算时间减少近一半。结论 实验结果表明,本文方法能有效提高亚像元定位精度,同时降低时间复杂度。因为高光谱图像中均匀混合区域不同地物的分布空间相关性不强,因此本文方法适用于非均匀混合的高光谱图像的亚像元定位。     

一种快速电阻抗成像方法及其灵敏度研究

该文介绍了一种基于等位线反投影算法的快速电阻抗断层成像方法，并实现了一种以等位线反投影算法为基础的实用动态电阻抗断层成像方法，分别研究了简单、复杂场域电导率分布下，该重建算法对场域内目标的定位精度及其对场域内部电导率分布变化的灵敏度。研究结果表明：该重建算法可以准确对目标进行定位，而且成像速度快，具有一定的分辨率，对场域内电导率分布的变化具有较好的灵敏度，但该重建算法的图像分辨率仍有待进～步提高。该快速电阻抗成像方法非常适合于对脑血肿、脑水肿患者的实时图像监护，以帮助医生及时做出正确的诊断，这在医学上有着重要的意义。     

面阵CCD相机像移补偿技术

航空相机在拍照瞬间由于飞机的飞行运动和姿态变化而产生像移,要提高照相分辨率必须通过像移补偿来实现;在分析垂直拍照的面阵CCD相机像移产生原因基础上,确定了采用稳定平台与面阵CCD的TDI相结合的像移补偿方法,理论上计算了像移补偿后的像移残差小于3μm(即1/3像元),满足成像质量要求;根据实际的成像试验,证明文章论述的面阵CCD像移补偿技术是正确可行的。     

结合超分辨率重建的神经网络亚像元定位方法

遥感影像中普遍存在着混合像元,如何分析和解译混合像元一直是人们研究的热点。亚像元定位方法是将混合像元分解成为亚像元,并赋予不同的端元组分,以提高影像整体分类精度的一种技术。本文在神经网络亚像元定位模型的基础上,结合超分辨率重建理论,提出一种新型的BPMAP模型,在每一个类别的组成分图像与亚像元定位图像之间建立起高、低分辨率的观测模型,采用最大后验估计（MAP）算法对BP神经网络的定位结果进行约束,最终确定混合像元内部各组分合适的空间位置。通过对模拟的简单图像和长江三峡地区的ETM影像进行实验,结果表明,与神经网络模型相比,本文方法能够更加有效地解决亚像元定位的问题,进一步消除定位过程中产生的误差,提高精度。     

利用协同表示与神经网络的高光谱图像亚像元定位

针对小样本情况下高光谱图像亚像元定位精度有限的问题,提出利用协同表示与神经网络的高光谱图像亚像元定位算法。该算法以一幅低空间分辨率的高光谱图像和少量的训练样本作为输入,首先应用空间上采样和基于协同表示的分类技术获取一幅亚像元级类别标签图,同时应用基于协同表示的分类、光谱解混和空间引力模型获取另一幅亚像元级类别标签图,之后依据两幅初始的亚像元级类别标签图扩充训练集,最后利用扩充后的训练集基于BP神经网络对高光谱图像进行亚像元定位,从而提高小样本情况下高光谱图像亚像元定位的精度。对于Indian Pines和Pavia University图像,所提算法的总体分类精度比ASPM算法分别高3.39%和9.63%,比ACSPM算法分别高0.26%和8.91%。实验结果表明,所提算法优于ASPM和ACSPM算法,尤其适用于细节信息较为丰富的高光谱图像。     

对偶四元数线阵遥感影像几何定位

提出以对偶四元数为数学工具进行线阵CCD(电荷耦合元件)遥感影像几何定位的全新技术方法。利用对偶四元数建立遥感通用传感器严密成像模型,将光线束的位置和姿态统一用对偶四元数表示,通过传感器扫描光线在空间中的螺旋运动,实现像点到其对应地面点物方坐标的变换,从而克服了成像几何参数(外方位元素)之间的强相关性。按照空间刚体变换线性蒙皮混合理论,可以把刚体变换矩阵分解为平移和旋转两个部分,对平移部分进行线性插值,对旋转部分进行球面插值,从而实现线阵CCD遥感影像外方位元素的解算。按照所建立的成像几何模型,利用某地区Geoeye-1遥感影像进行几何定位实验,实验结果表明新算法获得的几何定位精度优于传统算法,能够解决定位参数之间的相关性问题。     

基于轮廓特征点的高精度识别目标方法

为了实现高成功率、高精度和快速识别跟踪目标,提出对基于轮廓特征点的目标精确识别方法。在识别过程中采用了轮廓提取和多边形拟合算法自动搜寻到图像中要识别和跟踪的目标,同时对目标物轮廓的多边形角点进行亚像素分辨率的定位,从而可以利用目标轮廓角点的精确定位来实现对多边形目标的识别与跟踪。试验结果以及特征点亚像素算法分辨率的分析表明,采用这种自动识别与跟踪目标的方法,其精度可以达到0．02像素。     

基于小波分析和多项式细分定位的超分辨率图像重建算法

图像超分辨率重建是图像增强和图像复原研究中的一项重要课题,广泛应用于高清晰电视、医学成像和遥感成像等领域。在小波分析边缘检测的基础上,通过多项式细分算法定位亚像素边缘,将图像分为平滑区域、边缘区域和微细边缘区域。根据不同的区域特性,采用不同的插值方式进行超分辨率图像重建。仿真结果显示所提算法重建的高分辨率图像边界部分清晰自然,其主观判断和客观评价结果明显好于传统重建算法,从而验证了本算法的可行性和有效性。     

Zernike矩和最小二乘椭圆拟合的亚像素边缘提取

为了提高微操作系统的装配精度,提出了一种新型的亚像素边缘检测和中心定位算法。应用Canny算子提取了微零件在像素级的边缘。应用Zernike矩对微零件进行亚像素级的边缘定位。采用最小二乘椭圆拟合定位微零件的中心位置。实验结果表明,该算法能够实现更高的定位精度和消耗更少的时间。     

Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients

Sub-pixel mapping and sub-pixel sharpening are techniques for increasing the spatial resolution of sub-pixel image classifications. The proposed method makes use of wavelets and artificial neural networks. Wavelet multiresolution analysis facilitates the link between different resolution levels. In this work a higher resolution image is constructed after estimation of the detail wavelet coefficients with neural networks. Detail wavelet coefficients are used to synthesize the high-resolution approximation. The applied technique allows for both sub-pixel sharpening and sub-pixel mapping. An algorithm was developed on artificial imagery and tested on artificial as well as real synthetic imagery. The proposed method resulted in images with higher spatial resolution showing more spatial detail than the source imagery. Evaluation of the algorithm was performed both visually and quantitatively using established classification accuracy indices.     

Least Squares Sub-pixel Registration Refinement Using Area Sampler Model

Super-resolution applications require sub-pixel registrations of low resolution images to be almost exact due to the deterioration caused by inaccurate image registration. A linear-least-squares technique is proposed to refine sub-pixel translation parameters, which can be employed when the images are registered but just where there is not enough sub-pixel accuracy. In the technique, it is assumed that low resolution pixels are obtained by area sampling high resolution pixel field which have twice the density of their low resolution correspondents. Using this downsampling schema, a set of equations is formed. Assumed geometry and layout provide a constraint set to be used with the equation set. The sub-pixel translations are then found using least-squares-solution-with-equality-constraints. The method is shown to improve the registration accuracy.     

Soft-then-hard super-resolution mapping based on a spatial attraction model with multiscale sub-pixel shifted images

Super-resolution mapping (SRM) is a technique for exploring spatial distribution information of the land-cover classes at finer spatial resolution. The soft-then-hard super-resolution mapping (STHSRM) algorithm is a type of SRM algorithm that first estimates the soft class values for sub-pixels at the target fine spatial resolution and then predicts the hard class labels for sub-pixels. The sub-pixel shifted images from the same area can be incorporated to improve the accuracy of STHSRM algorithm. In this article, multiscale sub-pixel shifted images (MSSI) based on the fine-scale model and the coarse-scale model are utilized to increase the accuracy of STHSRM. First, class fraction images are derived from multiple sub-pixel shifted coarse spatial resolution images by soft classification. Then using the sub-pixel/sub-pixel spatial attraction model as fine-scale and the sub-pixel/pixel spatial attraction model as coarse scale, all MSSI can be derived from fraction images. The MSSI for each class are then integrated to obtain the desired fine spatial resolution images. Finally, the integrated fine spatial resolution images are used to allocate classes for sub-pixel. Experiments on two synthetic remote sensing images and a real hyperspectral remote sensing imagery show that the proposed method produces higher mapping accuracy result.     

Vectorial boundary-based sub-pixel mapping method for remote-sensing imagery

This article presents a vectorial boundary-based sub-pixel mapping (VBSPM) method to obtain the land-cover distribution with finer spatial resolution in mixed pixels. With inheritance from the geometric SPM (GSPM), VBSPM first geometrically partitions a mixed pixel using polygons, and then utilizes a vectorial boundary extraction model (VBEM), rather than the rasterization method in GSPM, to determine the location and length of each edge in the polygon, while these edges are located at the boundary of and within the interior of the mixed pixel. Furthermore, VBSPM uses a decay function to manage the mixed pixels along the image boundary region due to the missing parts of their neighbours. Finally, a ray-crossing algorithm is employed to determine the land-cover class of each sub-pixel in terms of vectorial boundaries. The experiments with artificial and remotely sensed images have demonstrated that VBSPM can reduce the inconsistency between the boundaries of different land-cover classes, approximately calculating errors with an odd zoom factor, and achieve more accurate sub-pixel mapping results than the hard classification methods and GSPM.     

基于形态学的十字目标定位方法研究

为了提高微操作系统的装配精度,提出了一种基于形态学腐蚀算法和Hough变换的十字目标亚像素中心定位方法。首先通过选取适当的结构元素,分别对组成十字图像的垂直、水平方向的直线段进行腐蚀处理,得到含有十字中心信息的水平和垂直的两条行、列像素,行、列的相交点即为十字图像特征的中点;然后选取适当参数空间,对行、列进行Hough变换并将结果记入参数空间累加器,最后对区域内点进行加权平均处理,得到十字图像亚像素中心定位。实验结果表明：该方法具有定位速度快,定位精度高的优点。     

多尺度卫星雪盖面积获取的对比研究

系统地开展尺度和尺度效应的研究,综合利用日益增多的不同分辨率的遥感影像数据,是地球空间信息科学发展的趋势之一。作为多尺度转换大命题中的前期工作,旨在通过试验的手段检验不同尺度产品的真实性,发现多尺度转换中潜在的各种问题,以及探索可行性的尺度转换方法,为进一步的多尺度转换研究工作提供良好的背景知识。多尺度雪盖面积的获取,包括两样区1 m分辨率野外人工子像元雪盖、两样区30 m分辨率子像元重采样雪盖、30 m Hyperion和TM卫星反演雪盖、以及MODIS 500 m分辨率的MOD10A1雪盖日产品。通过对上述不同尺度获取的雪盖面积的相互对比研究,我们发现：①1 m样区的雪盖>30 m重采样雪盖>30 m Hyperion和TM的雪盖 ;②若把1 m样区看做500 m像元的单点试验,该单点不能完全正确地表征同位置像元上的地物特征 ;③MOD10A1产品有云覆盖地区,宜采用前后雪盖合成的方法来辅助判断并恢复当日云层下的地表类型。同时,通过对各像元级尺度的雪盖面积的真实性检验,我们也发现尺度转换需关注的潜在关键问题：①精确的像元匹配 ;②重采样方式 ;③数据获取时间以及产品时间序列 ;④多传感器图像处理 ;⑤产品算法的影响 ;⑥混合像元的影响 ;⑦试验样方的大小设计 ;⑧地面同步物理参数的测量 ;⑨空间异质性的定量表达。     

Sub-pixel water temperature estimation from thermal-infrared imagery using vectorized lake features

Water skin temperature derived from thermal infrared satellite data are used in a wide variety of studies. Many of these studies would benefit from frequent, high spatial resolution (100 m pixels) thermal imagery but currently, at any given location, such data are only available every few weeks from spaceborne sensors such as ASTER. Lower spatial resolution (1 km pixels) thermal imagery is available multiple times per day at any given location, from several sensors such as MODIS on board both the AQUA and TERRA satellite platforms. In order to fully exploit lower spatial resolution imagery, a sub-pixel unmixing technique has been developed and tested at Quesnel Lake, British Columbia, Canada. This approach produces accurate, frequent high spatial resolution water skin temperature maps by exploiting a priori knowledge of water boundaries derived from vectorized water features. The pixel water-fraction maps are then input to a gradient descent algorithm to solve the mixed pixel ground leaving radiance equation for sub-pixel water temperature. Ground-leaving radiance is estimated from standard temperature and emissivity data products for pure pixels and a simple regression technique to estimate atmospheric effects. In this test case, MODIS 1 km thermal imagery was used along with 1:50,000 water features to create a high-resolution (100 m) water skin temperature map. This map is compared to a concurrent ASTER temperature image and found to be within 1 °C of the ASTER skin temperature 99% of the time. This is a considerable improvement over the 2.55 °C difference between the original MODIS product and ASTER image due to land temperature contamination. The algorithm is simple, effective, and unlocks a largely untapped resource for limnological and hydrological studies.     

Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation.Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties.     

Improving super-resolution mapping through combining multiple super-resolution land-cover maps

Super-resolution mapping (SRM) is an ill-posed problem, and different SRM algorithms may generate non-identical fine-spatial resolution land-cover maps (sub-pixel maps) from the same input coarse-spatial resolution image. The output sub-pixels maps may each have differing strengths and weaknesses. A multiple SRM (M-SRM) method that combines the sub-pixel maps obtained from a set of SRM analyses, obtained from a single or multiple set of algorithms, is proposed in this study. Plurality voting, which selects the class with the most votes, is used to label each sub-pixel. In this study, three popular SRM algorithms, namely, the pixel-swapping algorithm (PSA), the Hopfield neural network (HNN) algorithm, and the Markov random field (MRF)-based algorithm, were used. The proposed M-SRM algorithm was validated using two data sets: a simulated multispectral image and an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral image. Results show that the highest overall accuracies were obtained by M-SRM in all experiments. For example, in the AVIRIS image experiment, the highest overall accuracies of PSA, HNN, and MRF were 88.89, 93.81, and 82.70%, respectively, and these increased to 95.06, 95.37, and 85.56%, respectively for M-SRM obtained from the multiple PSA, HNN, and MRF analyses.