一种新颖的多视点图像规正算法

在多视点图像系统中,由于场景光照或相机标定的原因,通常会导致同一对象在不同视点位置颜色外表的不一致。传统的亮度补偿算法难以有效地解决这个问题。基于Retinex颜色恒常性理论,提出了一种新颖的多视点图像规正算法,通过直方图均衡化、Retinex处理和颜色恢复手段,提取出反映物体本质特征的反射光系数来消除不一致光照的影响,在增强单视点图像对比度的同时,将视点间图像的颜色规正到一致的水平。     

Similarity-enhanced target detection algorithm using polarimetric SAR images

Target detection and analysis using polarimetric synthetic aperture radar (PolSAR) images are currently of great interest in synthetic aperture radar (SAR) applications. For a complex target, the scattering characteristics are determined by different independent sub-scatterers and their interaction; therefore, the scattering characteristics should be described by a statistical method due to randomness and depolarization. Furthermore, the inherent speckle in SAR data must be reduced by spatial averaging at the expense of loss of spatial resolution. The polarimetric similarity parameter (PSP) is an effective parameter to analyse target characteristics. In order to describe a complex distributed target, two new methods for calculating PSP are proposed, namely Stokes matrix-based PSP (S-PSP) and multiple PolSAR similarity parameter (MPSP). The characteristics of a target can be described and extracted on the basis of the polarimetric similarity, and then the similarity-enhanced target detection methods using S-PSP and MPSP are implemented and demonstrated with German Aerospace Centre (DLR) experimental SAR L-band multiple temporal PolSAR images of Oberpfaffenhofen test site (DE), Germany. The results confirmed that the proposed methods are effective for detection and analysis of buildings in urban areas.     

Simplified radar mapping equations for terrain correction of space-borne SAR images

Correction of a space-borne Synthetic Aperture Radar (SAR) image for the effects of terrain distortion requires the use of mapping equations that relate the coordinates of a scatterer in three dimensions to its position in the twodimensional image. These mapping equations are complicated since they take into account the curved geometry of the Earth's surface, and since they must be evaluated of the order of 10 to 100 million times in the course of correcting a typical SAR image, the complexity is a disadvantage. In this paper we derive two approximations, one quadratic and one linear, to the mapping equation, and evaluate their performance relative to the observational parameters of a variety of space-borne SAR systems and the range of topographic variation present in the scene. We show that the quadratic approximation is sufficiently accurate in virtually all circumstances likely to be encountered. In most cases the linear approximation is also valid, although it is unsuitable where the topographic variation is large and the near-swath incidence angle is small.     

一种抑制SAR图像相干斑的空域滤波算法

为在保护SAR图像边缘特征的同时有效抑制乘性相干斑噪声,提出了一种空域滤波新算法。该算法以负指数衰减型加权滤波模型为基础,通过将SAR图像多种局部统计参量巧妙结合作为联合衰减因子,形成与SAR图像区域分布特性相适应的负指数型加权系数;同时采取两次滤波策略,先由预滤波削弱SAR图像相干斑噪声并估计获得更精准的局部统计参量,然后借助精细局部统计参量再对原SAR图像实施精细滤波。实验结果表明,与多种抑斑算法相比,该算法在SAR图像抑斑与边缘保护方面均获得了更好的性能。     

Radiometric correction in laser scanning

Scanning airborne Light Detection And Ranging (LiDAR) has gained considerable acceptance in both scientific and commercial communities as a tool for topographic measurement, becoming an industry‐standard tool for collecting high‐resolution topographic data. Once processed, the laser scan data consist of a series of values of coordinates of points at which the laser beam has been reflected. In addition to the coordinates for every single reflection, the backscattering amplitude is also recorded. We describe a methodology to achieve an accurate radiometric calibration of the laser amplitude data so as to generate pseudo‐reflectance maps of the surface illuminated by the laser. The pseudo‐reflectance maps have then been used to classify the target illuminated by the laser. The method can be considered as a potential aid to enhance the accuracy of laser data classification.     

StitchingPHm—A new algorithm for panoramic images

In this paper it is presented a new algorithm for the construction of panoramic images (including 360 panoramas), which has as main characteristic to avoid the distortion that occurs by joining of several successive images in a sequence. We used the SIFT and RANSAC algorithms to find overlap areas between pairs of images, as well as a Blend algorithm for smoothing the joints. As the proposed algorithm doesn’t cause distortions, the subsequent correction is not necessary, contributing to a better performance. The results of experiments using commercial software and also the proposed algorithm were compared through a visual analysis. In addition, a quantitative analysis was done using numeric measures calculated on a panoramic image, generated from an image sequence of a region mapped and georeferenced by Google Earth.     

基于卡方检验的SAR图像道路检测算法

为有效解决SAR图像道路检测算法中阈值优化的问题,提出了一种不需要固定阈值的检测方法.该方法由不同阈值组合的Tupin边缘检测算法得到边缘检测结果图集,并逐像素统计得到边缘密度图;引入卡方检验计算边缘密度图与边缘检测结果图集之间的相似性度量参数,优化得到最佳边缘相关等级;根据最佳等级得到最终的道路边缘检测结果.实验结果表明,该方法有较强的开放性与目标适应性,自动化程度高,有很强的工程实用性.     

NSCT变换的SAR和可见光图像融合

非下采样Contourlet变换同时具有多尺度、多方向选择性、多分辨分析和平移不变性质的特点,对SAR和彩色可见光图像融合的问题,提出一种基于非下采样Contourlet变换和HIS变换相结合的多传感器融合算法。通过对经非下采样Contourlet变换分解得到的不同频域子带系数选择方案的分析,对低频子带系数的选择,提出了一种基于SAR图像与彩色可见光图像物理特征的“加权平均”的系数选择方案;针对各带通方向子带系数的选择,采用绝对值选大多分辨率融合算法。实验以同一场景下ku波段的SAR图像和彩色可见光图像进行算法验证,实验结果和信息熵、均值、相关系数、偏差指数和交叉熵等客观评价数据表明,方法具有较好的融合效果。     

A segmentation algorithm for SAR images based on the anisotropic heat diffusion equation

A method toward unsupervised segmentation of synthetic aperture radar (SAR) images is proposed. In this method, the distribution of SAR intensity image and the maximum a posteriori (MAP) algorithm is used to obtain an initial segmentation. Then according to the equivalence between the solid heat diffusion model and image scale-space, multiscale anisotropic smoothing of the posterior probability matrixes is introduced to remove the influence of speckle and to preserve important structure information. The effectiveness of this algorithm is demonstrated by application to simulated and real SAR images.     

Radiometric slope correction for forest biomass estimation from SAR data in the Western Sayani Mountains,Siberia

We investigated the possibility of using multiple polarization (SIR-C) L-band data to map forest biomass in a mountainous area in Siberia. The use of a digital elevation model (DEM) and a model-based method for reducing terrain effects was evaluated. We found that the available DEM data were not suitable to correct the topographic effects on the SIR-C radar images. A model-based slope correction was applied to an L-band cross-polarized (hv) backscattering image and found to reduce the topographic effect. A map of aboveground biomass was produced from the corrected image. The results indicated that multipolarization L-band synthetic aperture radar (SAR) data can be useful for estimation of total aboveground biomass of forest stands in mountainous areas.     

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。     

SAR图像港口船只的实时解析算法

SAR图像较大难以实时运行且船只目标较小难以被识别,为此一种压缩级联深层神经网络算法被提出以实现对众多船只目标的分割定位识别。构建3个不同的卷积神经网络实现特征提取,引入级联结构融合不同网络输出的特征图实现网络的轻量化,融合后的特征输入金字塔池化模块实现特征细化,分类并解析。在Google Earth图像数据集中的实验结果表明,多分支网络的级联有助于大尺寸图像中目标特征的分散提取,分级的模型压缩有助于提升识别速度。     

自适应超完备字典学习的SAR图像降噪

提出一种基于自适应超完备字典学习的SAR图像降噪。该算法建立在超完备字典稀疏表示基础上,具有较强的数据稀疏性和稳健的建模假设。算法依据相干斑噪声统计特性,通过分步优化字典原子和变换系数自适应构造超完备字典,利用获得的超完备字典将图像局部信息投影到高维空间中,实现图像的稀疏表示,运用正则化方法建立多目标优化模型。最后通过对优化问题的求解重建SAR图像场景分辨单元的平均强度,实现SAR图像的降噪。实验结果表明,该算法对相干斑噪声有很好的抑制效果,并且具有保持图像细节信息的优点。     

Fast coastal algorithm for automatic geometric correction of AVHRR images

A coastal algorithm for fully automatic geometric correction of Advanced Very High Resolution Radiometer (AVHRR) images is presented. Inputs are the AVHRR image and updated ephemeris data and outputs are the georeference image and a cloud image mask. Its principal advantage and novelty is that it requires only manual control in the first stage of the process. Particularly, the detection of Ground Control Points (GCPs), usually rather time consuming, is performed with this method in an automatic way. The procedure only requires the previous existence of a coastal reference-windows database. To find the exact location of the GCPs, the routine searches the best match of these referencewindows with the image. The complete automation of the process makes the routine very fast, then allowing its operative application on a large volume of images. The process provides accuracies to within 1-1.5 AVHRR pixels.     

A statistical and Wiener filtering algorithm based on the curvelet transform for SAR images

Synthetic aperture radar (SAR) images contain many kinds of noise. Speckle noise is multiplicative noise generated by the coherent imaging processes involved in SAR images and brings a great hindrance to the interpretation and application of SAR images, so it is considered the first major kind of noise in SAR images. SAR images also contain other incoherent additive noises generated by other factors, such as Gaussian noise, which are all considered the second major kind of noise. In order to reduce the impact of noise as much as possible, after an in-depth study of SAR imaging and noise-generating mechanism, curvelet transform principle, and Wiener filtering characteristic, a novel filtering method, here called the statistical and Wiener based on curvelet transform (SWCT) method is proposed. The SWCT algorithm processes two different kinds noise based on their properties. Specifically, it establishes a two-tiered filtering framework. For the first kind of noise, the algorithm uses the curvelet transform to decompose the SAR image and uses the statistical characteristics of the SAR image to generate an adaptive filtering threshold of the coefficients of decomposition to recover the original image. Then it filters every sub-band image at each decomposed scale and performs the inverse curvelet transform. The second kind of noise is directly filtered using the Wiener filter in the SWCT algorithm. Using the two-tiered filtering model and fully exploiting statistical characteristics, the SWCT algorithm not only reduces the amount of coherent speckle noise and incoherent noise effectively but also retains the edges and geometric details of the original SAR image. This is very good for target detection, classification, and recognition. Qualitative and quantitative tests were performed using simulated speckle noise, Gaussian noise, and real SAR images. The proposed SWCT algorithm was found to remove noise effectively and the performance of the algorithm was tested and compared to the mean filter, enhanced gamma-MAP (maximum a posterior probability) filter, wavelet transform filter, Wiener filter, and curvelet transform filter. Experiments carried out on real SAR images confirmed that the new method has a good filtering effect and can be used on different SAR images.     

A new chain-coding algorithm for binary images using run-length codes

This paper presents a new algorithm for converting a binary image into chain codes using its run-length codes. The basic idea of conventional chain-coding algorithm is to follow boundary pixels by convolving a 3 × 3 window with the image and to sequentially generate chain codes. The proposed algorithm has two phases, namely run-length coding and chain-code generation. We use connectivity information between runs as well as their coordinates in the phase of run-length coding. In the second phase (chain-code generation) the connectivity information extracted in the first phase is utilized for sequentially tracking runs containing the boundary pixels to be followed. This algorithm has an advantage that we can detect easily the inclusion relationship between boundaries at the same time as chain-code generation.     

An improved speckle-reduction algorithm for SAR images based on anisotropic diffusion

This paper mainly studies the algorithm of anisotropic diffusion for speckle noise removal of SAR images. Because the Gauss curvature driven diffusion method is sensitive to the noise and is of low efficiency on suppressing the speckle noise, an improved denoising algorithm is proposed. The new algorithm introduces the difference curvature as the diffusion coefficients of the function, which solves the problem that Gauss curvature driven diffusion is sensitive to the speckle noise, further, Tukey’s biweight function is used to control the curvature diffusion model, which can not only better protect edges, but also automatically control the diffusion. Numerical experiments show that the improved algorithm can preserve the information of textures, edges while inhibiting the speckle of SAR images.     

Topology correction of segmented medical images using a fast marching algorithm

We present here a new method for correcting the topology of objects segmented from medical images. Whereas previous techniques alter a surface obtained from a binary segmentation of the object, our technique can be applied directly to the image intensities of a probabilistic or fuzzy segmentation, thereby propagating the topology for all isosurfaces of the object. From an analysis of topological changes and critical points in implicit surfaces, we derive a topology propagation algorithm that enforces any desired topology using a fast marching technique. The method has been applied successfully to the correction of the cortical gray matter/white matter interface in segmented brain images and is publicly released as a software plug-in for the MIPAV package.     

A target detection algorithm for SAR images based on regional probability statistics and saliency analysis

In comparison with optical images, a Synthetic Aperture Radar (SAR) image has many defects, such as low resolution, strong noise interference and random distribution of the target, which increases the false alarm rate of traditional detection methods. To improve the detection accuracy of the SAR image, a novel detection method is proposed based on regional probability statistics and saliency analysis. A saliency analysis model based on dense and sparse reconstruction (DSR) is reconstructed to locate the target precisely. Firstly, the regional probability of the SAR image is estimated to extract the background region. And then, the extracted background sub-blocks are clustered and employed to replace the corresponding background template set of the DSR model. Subsequently, the reconstructed DSR model is used to extract the target, and the detection accuracy of the proposed method is enhanced greatly. Compared with the constant false alarm rate (CFAR)-based detection method, the proposed method can achieve a high detection accuracy and protect the edges of the SAR image.     

高分辨率SAR图像线状目标提取方法研究

本文针对高分辨率SAR影像进行城区河流、道路提取的算法研究,提出将纹理、面状和空间区域三种特征结合的方法。首先基于多尺度分析技术,结合面向对象的分割算法提取初始目标,之后使用灰度共生矩阵对目标及周边背景纹理进行差异对比分析去除误目标。与传统边缘检测方法相比,本文方法充分利用高分辨率SAR影像带来的纹理细节信息,并综合考虑目标的多种特征因素,增强了目标提取的可靠性,实用性强。