Unsupervised urban area extraction from SAR imagery using GMRF

This paper presents a new method for unsupervised urban area extraction from SAR imagery using two different GMRF models. One model is the T-based GMRF model proposed by Xavier Descombes specially for acquiring urban area in panchromatic SPOT imagery. When it is used for urban area extraction from SAR imagery, some missing detection occurs. The other model is the conventional GMRF model that requires training samples for urban area extraction. When it is used for SAR imagery, the extraction result includes all urban areas and some false detection. Three steps are made up in our method. First, we adopt a threshold for the T-based GMRF model parameter T to acquire the result of urban area extraction. Then, taking the result as training samples, we estimate the conventional GMRF model parameters and acquire a new result of urban area extraction. Finally, we fuse the two results above using a region-growing algorithm to form the final accurate urban area extraction. Experimental results show that the proposed unsupervised approach can obtain accurate urban area delineation. The text was submitted by the authors in English. Yang Yong. Born in 1978. Now a postgraduate in the Department of Communication and Information Systems, School of Electronic Information, Wuhan University. The research direction is Image Processing. Scientific interestsare SAR image segmentation and classification with the Markov random field approach. Hong Sun. Born in 1954. Graduated from the Huazhong University of Science and Technology of Electrical Engineering in 1982. Received a Doctoral degree in 1995. Author of Advanced Digital Signal Processing, which is widely used as a textbook for graduated students in China. Scientific interests include statistical signal processing, image analysis, and communication signal processing. Yongfeng Cao. Born in 1976. Graduated from Wuhan University of China in 1999. Assistant and doctoral candidate in the laboratory of Signal Processing and Modern Communication, School of Electronic Information, Wuhan University, China. Scientific interests include Markov random fields, Watershed transformation, and SAR image interpretation.     

Automated delineation of coastline from polarimetric SAR imagery

In this paper we present a new diffusion-based method for the delineation of coastlines from space-borne polarimetric SAR imagery of coastal urban areas. Both polarimetric filtering and speckle reducing anisotropic diffusion (SRAD) are exploited to generate a base image where speckle is reduced and edges are enhanced. The primary edge information is then derived from the base image using the instantaneous coefficient of variation edge detector. Next, the resulting edge image is parsed by a watershed transform, which partitions the image into disjoint segments where the division lines between segments are collocated with detected edges. The over-segmentation problem associated with the watershed transform is solved by a region merging technique that combines neighbouring segments with similar radar brightness. As a result, undesired boundary segments are eliminated and true coastlines are correctly delineated. The proposed algorithm has been applied to a space-borne polarimetric SAR dataset, demonstrating a good visual match between the detected coastline and the manually contoured coastline. The performance of the proposed algorithm is compared with those of two polarimetric SAR classification algorithms and two edge-based shoreline detection methods that are tailored to single polarization SAR images. Experimental results are shown using polarimetric SAR data from Hong Kong.     

高分辨率SAR影像形态学层级分析的建筑物检测

目的 现有基于结构分析的高分辨率SAR影像建筑物检测方法,只考虑了直线和L形结构建筑物,并且依赖建筑物高亮线条处阴影区作为建筑物识别的主要特征;当处于复杂场景时,阴影区受制于背景较暗或建筑物密集而无法准确得到,导致建筑物检测误差大、检测率低。针对上述问题,提出一种基于形态学层级分析的高分辨率SAR影像无监督建筑物检测算法。方法 该方法基于单幅单极化高分辨率SAR影像,首先利用改进的形态学交替滤波算子有效抑制其固有的斑点噪声,大大剔除了同质区背景噪声的干扰;然后利用层级分析形态学差分属性断面算法来实现对SAR影像建筑物的几何结构特征的提取;最后结合特征融合和属性阈值分割等后处理步骤得到复杂场景下建筑物提取信息。结果 将上述方法在建筑物密集的城区SAR影像中实验,通过与其他方法对比分析,具有检测率高、误差小的特点,准确率和召回率分别为95.38%、86.31%,并对降低虚警率方面有明显的优势。结论 将形态学交替滤波与形态学属性滤波的改进与结合,在对不同走向、尺寸和形状的高密度建筑物检测中具有较好的适应性。     

基于物理散射模型的全极化SAR图像增强滤波算法

针对传统的极化SAR滤波方法图像中城镇区域和植被区域地物在滤波中易被混淆, 导致滤波后图像中地物边缘保持效果下降的问题, 提出了一种增强的保持极化散射特性的滤波算法。利用一种增强的四分量极化分解方法获取更加精确的地物散射机制, 并将散射机制信息引入滤波方法中, 使滤波算法中像素的散射机制更精确。增强的四分量极化分解方法引入了极化SAR数据的定向角补偿技术、一种新的体散射模型以及两种散射功率限制条件, 来改进Freeman-Durden分解的结果。理论分析和实验结果表明, 改进后的方法获取了比传统的极化SAR图像滤波算法更加理想的计算结果。     

High-resolution SAR images segmentation using NSCT denoising and QIGA based parameters selection of PCNN model

The high-resolution synthetic aperture radar (SAR) images usually contain inhomogeneous coherent speckle noises. For the high-resolution SAR image segmentation with such noises, the conventional methods based on pulse coupled neural networks (PCNN) have to face heavy parameters with a low efficiency. In order to solve the problems, this paper proposes a novel SAR image segmentation algorithm based on non-subsampling Contourlet transform (NSCT) denoising and quantum immune genetic algorithm (QIGA) improved PCNN models. The proposed method first denoising the SAR images for a pre-processing based on NSCT. Then, by using the QIGA to select parameters for the PCNN models, such models self-adaptively select the suitable parameters for segmentation of SAR images with different scenes. This method decreases the number of parameters in the PCNN models and improves the efficiency of PCNN models. At last, by using the optimal threshold to binary the segmented SAR images, the small objects and large scales from the original SAR images will be segmented. To validate the feasibility and effectiveness of the proposed algorithm, four different comparable experiments are applied to validate the proposed algorithm. Experimental results have shown that NSCT pre-processing has a better performance for coherent speckle noises suppression, and QIGA-PCNN model based on denoised SAR images has an obvious segmentation performance improvement on region consistency and region contrast than state-of-the-arts methods. Besides, the segmentation efficiency is also improved than conventional PCNN model, and the level of time complexity meets the state-of-the-arts methods. Our proposed NSCT+QIGA-PCNN model can be used for small object segmentation and large scale segmentation in high-resolution SAR images. The segmented results will be further used for object classification and recognition, regions of interest extraction, and moving object detection and tracking.

     

SAR图像中海上舰船目标自动检测新方法

针对中分辨率近岸海域SAR图像,结合已有的舰船检测算法,提出了一种新的海上舰船目标自动检测方法。该方法先根据相应的抽取算法和图像数据映射准则,分离图像中的海洋和陆地区域,并结合最大熵分割法提取海洋背景中包含候选目标的感兴趣区域,最后利用特征匹配方法检测出真正的舰船目标。对50多幅SAR图像进行了试验,其结果表明该方法能自动、快速、准确地检测出图像中舰船目标。     

结合张量投票和Snakes模型的SAR图像道路提取

目的 Snakes模型对曲线轮廓具有良好的拟合能力,被广泛应用于遥感图像的道路提取。但SAR图像受乘性斑点噪声影响严重,因此利用Snakes模型从SAR图像提取道路时,传统的以图像灰度负梯度为外部能量的方法难以取得理想结果。针对这一问题,利用计算机视觉中的张量投票算法可以从噪声掩盖的图像中提取显著结构特征的特点,将张量投票与Snakes模型结合从SAR图像提取道路。方法 首先利用模糊C均值分割法从SAR图像中分割出道路类,然后对道路类进行张量投票获得每点的曲线显著性值,最后以该曲线显著性值的负值作为Snakes模型外部能量从SAR图像提取道路。在Snakes模型能量最小化阶段,提出了一种优化的拟合策略,一边内插节点一边最小化Snakes模型能量。结果 利用机载和星载不同场景的SAR图像进行实验,与同类的基于Snakes模型的半自动方法相比,本文方法对曲率较大的道路仅需较少控制点即可取得较好的拟合效果;与基于MRF模型的自动方法相比,本文方法对道路提取的完整率、正确率、检测质量都优于基于MRF模型的方法,并且提取的时间远远快于基于MRF模型的方法,对于大范围的道路网提取将更为实用。结论 本文方法充分考虑到道路的几何形态特征,利用张量投票算法对该特征进行量化,并利用优化的拟合策略来最小化Snakes模型能量来提取道路。基于机载和星载SAR图像的实验表明本文方法可以较好地提取不同场景中的主要道路目标和道路网。     

混合本征模型的多视SAR影像海冰密度检测

目的 SAR影像中像素光谱测度的空间相关性蕴含着海洋表面和海冰更加丰富的空间特性及其变化信息,因此合理建模这种相关性是高分辨率SAR影像海冰精准解译的关键。提出一种利用随机模型及空间统计学测度刻画海冰空间结构的方法。方法 本文首先,在空间统计学框架下,SAR影像被表示为多值Gamma模型和泊松线Mosaic模型线性加权构建的混合模型,其中多值Gamma模型用于描述海洋表面雷达信号背向散射变化的连续性,而泊松线Mosaic模型则用于表征不同类型海冰表面雷达信号背向散射变化的区域性。利用上述混合模型的一阶、二阶变异函数,建模蕴含在SAR影像中海冰空间结构的变化。结果 对RADARSAT-1影像海冰结构建模并反演其密度。实验区域真实海冰密度分别为20%,80%等,运用本文方法反演所得海冰密度与真实海冰密度误差正负不超过10%。结论 本文提出混合本征模型用以刻画SAR强度影像中海冰像素强度变化的空间关系,能够较好地反演Ungava湾海冰密度分布。为利用遥感影像检测空间机构提供一种全新的方法。     

一种新型的空域SAR图像相干斑抑制方法

为了有效抑制SAR强度图像中的相干斑噪声,提出一种改进Sigma滤波并结合Gamma MAP滤波的空域相干斑抑制方法。首先利用阈值判断法判断并保留强点目标,然后结合SAR图像分布模型和MMSE准则判断Sigma区间,其中可以根据图像局部统计特性自适应调整窗口尺寸,最后选择Sigma区间内像素进行Gamma MAP滤波。实验结果表明:对于星载和机载SAR图像,在相干斑噪声抑制和边缘纹理细节信息保持方面,该方法较其他常用的空域相干斑抑制方法具有明显的优越性,能极大地提高SAR图像判读和目标识别能力。     

基于卡尔曼滤波的SAR图像边缘检测方法

针对传统 Canny 边缘检测算法对合成孔径雷达(SAR)图像的相干斑噪声抑制程度 太高,导致大量边缘的真实信息丢失问题,提出一种新型 Canny 算子边缘检测算法。首先建立 合适的非对称半平面区域(NSHP)图像模型,将空间模型转换成卡尔曼滤波可适用的系统状态方 程;然后用“预测+反馈”的方式对图像去噪;最后通过双阈值算法提取图像的边缘。仿真实验表 明,该方法可以有效地抑制 SAR 图像中的相干斑噪声,同时能较好地保留图像的边缘信息,相 对于传统的 Canny 算法有较好的检测效果。     

Unsupervised change detection in SAR images based on frequency difference and a modified fuzzy c-means clustering

Change detection for synthetic aperture radar (SAR) images is a key process in many applications exploiting remote-sensing images. It is a challenging task due to the presence of speckle noise in SAR imaging. This article investigates the problem of change detection in multitemporal SAR images. Our motivation is to avoid using only one detector to measure the change level of different features which is usually considered by classical methods. In this article, we propose an unsupervised change detection approach based on frequency difference in wavelet domain and a modified fuzzy c-means (FCM) clustering algorithm. First, the proposed method extracts high-frequency and low-frequency components using wavelet transform, and then constructs high-frequency and low-frequency difference images using different detectors. Finally, inverse wavelet transform is carried out to obtain the final difference image. In addition, inspired by manifold structure constraint, we incorporate weighted local information into the FCM to reduce the influence of speckle noise. Experimental results performed on simulated and real SAR images show the effectiveness of the proposed method, in terms of detection performance, compared with the state-of-the-art methods.     

A novel Scan SAR imaging method for maritime surveillance via Lp regularization

In this article, a novel Scan mode synthetic aperture radar (SAR) imaging method for maritime surveillance is presented. Conventional Scan SAR is generally operated with severe azimuth resolution loss in order to cover a large area. The proposed imaging method changes the way Scan SAR illuminates sub-scenes and presents a new radar illuminating strategy based on ships’ spatial distribution in each sub-scene. To gain ships’ spatial distribution, a scene sensing algorithm based on radar range profiles together with a peak-seeking and clustering algorithm is introduced. After that, a Markov transfer-probability matrix is generated to make sure that radar illuminates each sub-scene randomly under the probability we calculated before. Finally, an imaging algorithm within the L_p (0 < p ≤ 1) regularization framework is utilized to reconstruct each sub-scene; the regularization problem is solved by an improved iterative thresholding algorithm. The whole wide swath image is joined by putting all the sub-scenes together. Experimental results support that the proposed imaging method can perform high-resolution wide swath SAR imaging effectively and efficiently without reducing the image resolution.     

基于多视角学习策略的手部姿态估计

手部姿态估计在人机交互、手功能评估、虚拟现实和增强现实等应用中发挥着重要作用, 为此本文提出了一种新的手部姿态估计方法, 以解决手部区域在大多数图像中占比较小和已有单视图关键点检测算法无法应对遮挡情况的问题. 所提方法首先通过引入Bayesian卷积网络的语义分割模型提取手部目标区域, 在此基础上针对手部定位结果, 利用所提基于注意力机制和级联引导策略的新模型以获得较为准确的手部二维关键点检测结果.然后提出了一种利用立体视觉算法计算关键点深度信息的深度网络, 并在深度估计中提供视角自学习的功能. 该方式以三角测量为基础, 利用RANSAC算法对测量结果进行校准. 最后经过多任务学习和重投影训练对手部关键点的3D检测结果进行优化, 最终提取手部关键点的三维姿态信息. 实验结果表明: 相比于已有的一些代表性人手区域检测算法, 本文方法在人手区域检测上的平均检测精度和运算时间上有一定的改善. 此外, 从本文所提姿态估计方法与已有其他方法的平均端点误差(EPE_mean)和PCK曲线下方面积(AUC)这些指标的对比结果来看, 本文方法的关键点检测性能更优, 因而能获得更好的手部姿态估计结果.     

引入欧氏距离的各向异性扩散相干斑抑制

目的 相干斑噪声严重影响SAR影像解译。抑制相干斑同时,获取较好的边缘保持效果始终是相干斑抑制的重点。针对该问题,提出一种引入欧氏距离的各向异性扩散（EDAD）相干斑抑制方法。方法 EDAD算法以P-M模型与SRAD算法为基础,利用邻近像素间区域欧氏距离代替原有边缘检测算子,自适应区分同质区与异质区,有效构造各向异性扩散系数,完成相干斑抑制。结果 运用EDAD算法与现存各向异性扩散算法对截取的两景TanDEM-X影像进行试验研究并比较各类算法的评估参数。EDAD算法的等效视数分别为3.996与5.859,均高于其他算法,体现优越的相干斑抑制能力;EDAD算法相干斑抑制前后比值影像的均值分别为0.999与1.001,方差分别为0.270与0.269,较其他算法均更接近理想值1与0.273,展现更优边缘保持与相干斑抑制能力。结论 本文算法可有效提高边缘检测能力,获取更优相干斑抑制效果。经验证,对分布较散的弱相干斑区域与分布较集中的强相干斑区域均有较好适用性。     

基于遥感图像三区光谱特征的水网城市区域规划协调控制方法

水网城市区域规划功能区图像相似性特征较强，且受生态环境、水资源调度等多个因素影响，造成水网区域规划协调性较差，提出基于遥感图像三区光谱特征的水网城市区域规划协调控制方法。使用高分辨率遥感设备采集城市影像；运用形态学算法，利用结构树，通过击中、击不中变换的方式对图像做细化处理；从亮度均值、目标标准差和亮度差级指数三方面提取图像光谱特征；采用最邻近分类算法，建立分类模型，以优先功能区、一般功能区和冲突协调区的自身环境和光谱特征为依据，将提取的特征输入到分类模型中，自动输出划分结果，利用 A*算法简化划分步骤，实现水网城市规划协调控制。实验结果表明，所提方法提高了遥感图像质量，分类结果符合不同功能区的特征要求；兼顾其它生态要素，规划具有协调性，规划效果较好；可在2s内完成目标区域规划，规划时间较短；对于目标规划数据的检测误差小、准确率高，大幅度提高搜索效率，能够为水网城市区域规划协调控制提供可靠依据。     

基于目标的低码率SAR图像压缩

基于目标的SAR图像压缩关键问题是将自动目标检测与图像压缩算法相结合。提出了以db4小波进行小波域多分辨率恒虚警率（CFAR）检测并嵌入图像编码中,在压缩有损量化前检测出目标区,推导了重要小波系数掩膜公式并据此将每个子带中的系数分成目标与背景两个序列,对目标序列以高比特率ECTCQ编码而背景序列则相反。对MSTAR图像压缩实验结果表明,同一般的SAR图像编码算法W/TCQ相比,低码率下TIC算法目标区SNR高,同时背景信息亦得到保护。     

全极化SAR影像城区建筑密度分析

针对全极化SAR影像的建筑区特性,提出了一种基于极化特征共生矩阵的城区建筑密度分析方法。首先将极化特征与共生矩阵结合,在考虑建筑区极化散射机理和建筑朝向作用的同时,兼顾了建筑区的空间排列信息,在此基础上为了增强建筑密度的局部区域特性,将共生矩阵特征进行K-means聚类,结合图像分块形成标号直方图统计矢量,进而对该直方图统计矢量进行矢量量化实现SAR影像城区的建筑密度分级。RadarSat-2全极化SAR影像城区建筑密度分析的实验表明,该方法既适用于建筑朝向复杂城区也适用于建筑排列整齐城区的密度信息提取。     

K近邻优化估计的SAR图像建模与目标检测算法

在非均匀杂波环境下的合成孔径雷达(synthetic aperture radar, SAR)图像背景建模问题中,针对非参量建模算法Parzen窗估计严重依赖于窗宽设置及最优核函数选择的问题,提出一种基于K近邻优化的概率密度函数估计算法,解决因固定近邻数而导致估计不准确甚至不能估计的问题.该算法不需要图像的任何先验知识,且无需考虑窗宽的设置及最优核函数的选择问题.与Parzen窗估计、K分布和G⁰分布的对比实验表明,所提出的K近邻优化估计算法可以实现对单峰、多峰甚至不规则图像数据的准确建模,优于K分布和G⁰分布;同时,对图像首尾数据的处理优于Parzen窗估计.实验结果验证了所提出方法对SAR图像杂波建模的精确性、鲁棒性和简便性,以及全局恒虚警率目标检测的有效性.     

一种SAR图像机动目标检测与分析方法

从SAR(合成孔径雷达)图像中检测和分析目标是进行SAR自动目标识别的关键步骤,提出了一种SAR图像中地面机动目标检测与分析的方法,该方法在对图像进行预处理后首先利用背景杂波强度分布为指数分布假设的恒虚警率算法以及形态学运算对原始的SAR场景数据进行快速检测获得感兴趣的目标区域,然后提取目标区域8个特征构成特征矢量以详细描述目标。实验结果表明,该方法计算速度快,能够从获得的目标区域得到大量有用的信息,而且该方法具有一定的通用性。     

A sensitivity analysis of dual-polarization features to damage due to the 2016 Central-Italy earthquake

ABSTRACT

In this study, the sensitivity of dual-polarization features extracted from dual-polarimetric coherent synthetic aperture radar (SAR) imagery to urban damage due to the earthquake occurred in Central -Italy in 2016 is addressed. First, a conventional single-polarization feature, based on the coherence between pre- and post-event co-polarized imagery, is analysed using SAR scenes collected by the Copernicus Sentinel-1 mission over the area of Amatrice, an Italian city significantly damaged by the 2016 earthquake that affected Central Italy. Then, the same analysis is undertaken using the cross-polarized channel to point out that the joint use of co- and cross-polarized channels can help in better observing the damaged area. Finally, a coherent dual-polarimetric feature, based on the inter-channel coherence, is proposed. This feature results in a sensitivity to damaged areas comparable to the one provided by conventional single-polarization features. However, the interchannel coherence has the benefit to be based only on two SAR acquisitions, i.e. before and after the earthquake and it is less affected by false alarms. SAR, polarimetry, earthquake, losses