基于空间关系几何约束的无人机景象匹配导航*

针对GPS的信号强度较弱、易受各种电磁干扰,提出一种基于空间关系几何约束的景象匹配导航算法。首先基于空间关系几何约束的多匹配区选择方法,将实时图划分为多个分区;然后采用基于边缘响应的加权Hausdorff距离景象匹配算法对各个匹配区进行定位计算;最后,通过最优配准点坐标估计策略,解算出无人机实时图中心在基准图上的精确定位坐标。多区域景象匹配采用并行计算方法,利用历史导航信息来辅助修正景象匹配导航误差。实验结果表明,该算法可较好地满足无人机对景象匹配导航算法实时性、精确性的性能要求。     

一种抗几何畸变的景象匹配方法

在分析景象匹配实时图获取过程中几何畸变产生原因的基础上,利用弹上高度传感器和姿态角传感器获取的图像高度和姿态信息,实现了畸变实时图的几何校正,给出了校正实时图大小和偏移量的确定方法,结合匹配计算和匹配结果修正,提出了一种抗几何畸变的景象匹配方法。仿真实验结果验证了该方法的有效性和实用性。     

基于ROC分析的Canny算法在景象匹配中的应用

根据ROC分析方法能对分类识别算法进行多门限评估的特点,利用其改进Canny算法性能并应用于景象匹配。首先由不同参数组合的Canny算子计算图像的多个边缘提取图,并逐像素进行统计,得到边缘像素相关图;然后采用ROC曲线分析找到最佳的关联阈值,从而确定理论边缘图;最后将参考图与实时图所对应的理论边缘图进行景象匹配。实验结果表明,该方法在参考图和实时图存在一定几何畸变和灰度差异的情况下,能取得较高的平均匹配精度与正确匹配概率。此外,该方法克服了传统Canny算子采用固定参数的缺点,根据多参数自动进行筛选优化,有较强的工程实用性。     

N帧连续景象匹配一致性决策算法

下视景象匹配是各类巡航飞行器实现精确导航的重要手段,它是通过事先装载在飞行器的地面基准图像与实时获取的地面景象图进行相关匹配,完成飞行器的飞行误差修正和精确定位,由于基准图和实时图在获取手段、时间上等方面的差异,在相关匹配中,配准点往往落在相关阵的次峰上,导致匹配失败,该文提出了多帧连续景象匹配,利用单次匹配后形成的相关阵中的主次峰信息与导航信息进行一致性决策的图论算法,由于引入了相关阵中的次峰信息,并结合惯导位移进行一致性判断,使得匹配算法能有效剔除误匹配点,从而提高了算法的匹配概率和鲁棒性。     

一种新的实时图加窗中值滤波算法

实时图的匹配性能是影响景象匹配精度的重要因素,然而实时图在获取过程中,由于受各种干扰因素的影响,图像存在模糊、毛糙等现象,影响了实时图的匹配性能。结合空域滤波方法中最常用的中值滤波器和邻域均值滤波器的特点,设计了一种加窗中值滤波器,并将其应用于景象匹配实时图的滤波预处理,通过仿真实验验证了该方法的有效性和在景象匹配中的实用性,可有效提高实时图的信噪比,改善其匹配性能。     

一种有效抑制机载传感器成像畸变干扰的景象匹配算法

电荷耦合器件(CCD)与合成孔径雷达(SAR)是两种最常用的机载传感器,是景象匹配辅助导航系统的关键组成部分.当系统传感器获取的实测图存在严重成像畸变时,传统的基于特征点集间Haus-dorff距离测度的匹配算法精度较低,通过引入多尺度自卷积(MSA)这种具有仿射不变性的局部特征,并将其同图像的尺度不变变换(SIFT)特征串行组合可以得到一种更加鲁棒的匹配算法.采用CCD和SAR传感器获取的图像对算法进行了仿真验证,实验结果表明:引入MSA特征对抑制景象匹配算法中多源传感器成像畸变的干扰十分有效,匹配精度显著提高.     

基于SIFT特征的合成孔径雷达景象匹配方法

根据合成孔径雷达图像的特点,提出一种基于SIFT特征的合成孔径雷达(SAR)景象匹配的方法。首先利用改进的特征描述符初步提取实时图与参考图的SIFT 关键点;然后利用距离比和RANSAC算法去除错配,匹配出可靠的同名点对;最后计算反映实时图和参考图之间变换关系的转换参数,完成景象匹配。实验结果表明,本方法快速实用,有较强的有效性和鲁棒性。     

快速并行两级景象匹配方法

文章分析了景象匹配系统的性能评价以及影响性能评价的三个主要因素,即实时图大小,参考图大小和匹配算法。分析了实时图大小的选取对匹配时间,匹配率的影响。并且针对巡航导弹的景象匹配制导系统的实时性问题,提出了一种快速并行两级景象匹配方法。该方法能够极大地提高匹配效率。试验中利用SAR图像和可见光图像进行景象匹配,每次匹配的时间从原来的约16秒减少到300毫秒左右,匹配率可达到90%以上。而且还具有可扩充性好的特点。     

景象间灰度变化的物理校正方法研究

根据Phong照射模型，从景象成家机理出发，研究了不同时间条件下，同一地区景象发生灰度变化的原因，并在一定的简化模型下，研究了利用成象时的人射角对景象匹配所用参考图与实时图间的灰度变化进行物理校正，以恢复二者间共性的灰度特征的方法。文中分析了参考图与实时图间不可预知的成象条件的差异对校正结果的影响，并通过校正前后参考图与实时图间信噪比的变化，从理论上证明了校正方法的有效性。最后给出了模拟实验结果。     

基于实时地形校正的InSAR图像匹配算法

针对侧视雷达/可见光图像匹配制导系统中由于雷达图像地形畸变引起的误匹配问题,提出了一种基于干涉合成孔径雷达（InSAR）的实时地形校正图像匹配算法。该算法以侧视雷达成像几何构象为基础,利用InSAR获取的实时地形数据对获取的SAR景象数据进行实时几何校正,生成无畸变的SAR景象数据,然后利用校正后的SAR景象数据与提前安装的可见光基准数据进行基于去均值归一化互相关模板的图像匹配。实验结果表明,通过实时地形校正,该景象匹配算法在复杂地形区域的匹配概率和匹配精度都大大优于传统SAR景象匹配算法,有效地提高了SAR图像匹配制导技术的适用性。     

Parallel spatiotemporal autocorrelation and visualization system for large-scale remotely sensed images

Current studies on large-scale remotely sensed images are of great national importance for monitoring and evaluating global climate and ecological changes. In particular, real time distributed high-performance visualization and computation have become indispensable research components in facilitating the extraction of remotely sensed image textures to enable mining spatiotemporal patterns and dynamics of landscapes from massive geo-digital information collected from satellites. Remotely sensed images are usually highly correlated with rich landscape features. By exploiting the structures of these images and extracting their textures, fundamental insights of the landscape can be derived. Furthermore, the interdisciplinary collaboration on the remotely sensed image analysis demands multifarious expertise in a wide spectrum of fields including geography, computer science, and engineering.     

Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.     

纸币清分系统中的纸币污损检测

针对纸币上常见的笔划及撕裂, 提出了一种基于均匀性特征的污损检测方法. 首先利用均匀性特征判定待检纸币上可能存在污损的区域, 然后进行图像配准, 确定这些区域在参考图像上的对应位置, 并逐像素进行比较, 最终判定待检图像的污损状况.     

An entropy-based image registration method using image intensity difference on overlapped region

Image registration is a fundamental procedure in image processing that aligns two or more images of the same scene taken from different times, different viewpoints, or even different sensors. It is reasonable to orientate two images by matching corresponding pixels or regions that are considered identical. Based on this concept, this paper proposes a novel image registration method that applies the information theorem on intensity difference data. An entropy-based objective function is then developed according to the histogram of the intensity difference. The intensity difference represents the absolute gray-level difference of the corresponding pixels between the reference and sensed images over the overlapped region. The proposed registration method is to align the sensed image onto the reference image by minimizing the entropy of the intensity difference through iteratively updating the parameters of the similarity transformation. For performance evaluation, the proposed method is compared with the two exiting registration methods in terms of eight test image sets. The experiment is divided into two scenarios. One is to investigate the sensitivity (i.e., robustness) of the objective functions in these three different methods; the other is to verify the effectiveness of the proposed method. Through the experimental results, the proposed method is shown to be very effective in image registration and outperforms the other two methods over the test image sets.     

Matching conjugate points between multi-resolution satellite images using geometric and radiometric properties

Remotely sensed images are the main source for a variety of mapping and change-detection applications. Images from different satellites are employed in several of these applications. However, each type of these images has a different resolution and orientation. Hence, they need to be co-registered before any meaningful use. The first step in the registration process is to find conjugate points between the images. This paper presents a modified method of the Scott and Longuet-Higgins approach to find conjugate points between different remotely sensed images. In such an algorithm, initially, corner points are automatically extracted in two images, and for each pair of points, a cost value is computed. The cost of corresponding any two points is computed using two-dimensional transformation models and pixel intensities. The cost values are then used to fill a cost matrix, and its singular value decomposition is used to find corresponding points. The algorithm is tested on three pairs of satellite images with different resolutions and orientations. The results show that the approach presented here succeeded in finding 93% of conjugate points between different pairs of satellite images using only the image coordinates through the eight-parameter transformation model. Moreover, the results show that including the image intensities in the matching procedure does change the results significantly.     

利用Photo Styler软件处理卫星遥感图像

介绍了Window环境下用图像处理软件PhotoStyler处理卫星遥感图像的方法和特色，Windows位图文件格式与遥感图像文件格式的转换，充分利用PhotoStyler的丰富功能能较好地完成遥感图像增强和加注汉字等前期和后期遥感图像处理工作。     

Enhancing remote sensing image retrieval using a triplet deep metric learning network

ABSTRACT

With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we present a novel content-based remote sensing image retrieval (RSIR) method based on Triplet deep metric learning convolutional neural network (CNN). By constructing a Triplet network with metric learning objective function, we extract the representative features of the images in a semantic space in which images from the same class are close to each other while those from different classes are far apart. In such a semantic space, simple metric measures such as Euclidean distance can be used directly to compare the similarity of images and effectively retrieve images of the same class. We also investigate a supervised and an unsupervised learning methods for reducing the dimensionality of the learned semantic features. We present comprehensive experimental results on two public RSIR datasets and show that our method significantly outperforms state-of-the-art.     

A fast and efficient image registration algorithm using outlier rejection technique based on subimage

Image registration is a process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and by different sensors. It geometrically aligns two images, the reference and sensed image. In this paper, a fast and efficient image registration algorithm is proposed for IDS (Intruder Detection System). To reduce a calculation time, outlier rejection method based on uniformity, entropy and subimage is used. An edge tapering method is applied to alleviate a boundary effect of a subimage. And it is shown that the proposed algorithm improves the accuracy and calculation time effectively.     

Assessing the effectiveness of Google Earth images for spatial enhancement of RapidEye multi-spectral imagery

RapidEye satellite images with high spatial resolution, affordable prices and having Red-Edge band have high potential for time series issues, especially in vegetation studies. Despite these beneficial properties, RapidEye images with 5 m spatial resolution are not sufficiently useful for some applications. According to this problem, enhancing the spatial resolution of RapidEye images can significantly improve the results of the subsequent processes on these images. Fusion of high spatial resolution with high spectral resolution images is known as an effective way to enhance the quality of multispectral remotely sensed images. Unfortunately, the lack of panchromatic band with high spatial resolution has been faced the procedure of improving the spatial resolution of RapidEye images with major problems. In this paper, we have proposed using the free Google Earth (GE) images which have high spatial resolution and high-coverage of land surface to enhance the spatial information of RapidEye images. A simulated panchromatic image has been generated by three band GE image and with three different methods: Mean, principal component analysis (PCA) and weighted average of GE image bands. In the last method, the weights are extracted from the spectral response curve of the satellite which captured the GE image. The simulated panchromatic image has been utilized for pansharpening of RapidEye image in five well-known methods: Brovey, Gram-Schmidt (GS), intensity-hue-saturation (IHS), Pansharp1 and Pansharp2. The most important point is finding the GE image with lowest lag time with RapidEye image. By satisfying this condition, the experiments illuminated that the proposed method can effectively enhance the spatial quality of RapidEye image. Also, this study presented that Pansharp2 method, which used simulated panchromatic image generated by the spectral response curve information, has revealed the best results of RapidEye image pansharpening.     

三峡库区遥感三维立体仿真系统及其应用

基于长江中上游(江津—宜昌段)地质调查项目完成的遥感影像、DEM、地质专题数据,构建了三维仿真系统。本系统通过对库区高精度三维地形地貌仿真和三维环境下的遥感影像、专题数据的一体化管理,实现了三维环境下的地质灾害人机交互遥感解译,以及在三维环境下的GIS空间分析和专业分析功能。通过两期三维场景对比、加载不同时期的影像数据、实时联动显示,直观清晰地开展研究区地质灾害、地形地貌、库岸线变化分析对比。系统大幅度提高了地质灾害遥感解译精度和工作效率,具有一定实际应用价值。