多尺度分割的高分辨率遥感影像变化检测

针对高空间分辨率的遥感影像,提出了一种基于多尺度分割的变化检测算法。采用Mean-Shift分割算法对影像进行多尺度分割,构建了不同尺度上的地理对象,以不同尺度上的地理对象灰度均值构建了变化检测的多尺度特征向量,采用变化矢量分析法获得最后的变化检测结果。以城镇区和农田区的Quick Bird影像对本文算法进行了检验,从精度评价的效果来看,无论城镇区还是农田区,采用面向对象的变化检测方法精度都高于基于单像素的检测方法,且当尺度层数固定时,多尺度组合的变化检测结果优于单一尺度的变化检测结果,对城镇、农田区域的变化检测的精度分别达到87.57%和81.55%。本文算法既可以顾及大面积同质区域变化,又可以反映小的地物目标及边缘部分的变化,能够很好地满足城镇、农田等不同环境背景下的变化检测需求,在国土资源监测中具有一定的应用价值。     

Contextual Analysis Based Approach for Detecting Change from High Resolution Satellite Imagery

Spatial contextual feature plays an important role in high resolution remote sensing image. A new approach based on local variance analysis is introduced to spatial contextual feature extraction for change detection in this paper. In the proposed approach, the change magnitude between the paired central pixels of a local area (e.g., a 3 × 3 sliding window) in multitemporal images depends on surrounding pixels in the difference image, and this change magnitude is quantitatively measured based on the standard deviation of the difference image within the local area. This difference image contextual property may be useful particularly when high-resolution images are used. Finally, the change magnitude image is classified as a binary CD map by using SVM. This proposed approach is applied to SPOT-5 multitemporal datasets and two QuickBird multitemporal images from two case studies, which are quantitatively compared and validated. Experimental results show that the proposed approach is feasible.     

SAR change detection based on intensity and texture changes

In this paper, a novel change detection approach is proposed for multitemporal synthetic aperture radar (SAR) images. The approach is based on two difference images, which are constructed through intensity and texture information, respectively. In the extraction of the texture differences, robust principal component analysis technique is used to separate irrelevant and noisy elements from Gabor responses. Then graph cuts are improved by a novel energy function based on multivariate generalized Gaussian model for more accurately fitting. The effectiveness of the proposed method is proved by the experiment results obtained on several real SAR images data sets.     

基于最优极化相干系数的倾斜建筑物解译研究

基于Freeman_Durden分解的全极化SAR影像分类方法能够较好地保持地物极化散射特性,但在分类的过程中,不能改变初始散射机制,导致分解结果对分类精度影响很大。在Freeman_Durden分解中,排列方向相对雷达飞行方向不平行的建筑物（简称为倾斜建筑物）常被分为体散射类型,使得该类建筑物往往被误分为植被。本文通过分析建筑物在SAR影像中的后向散射特性,利用建筑物具有较高相干性的特点,引入最优极化相干系数,在目标分解的基础上通过阈值分割将两者区分开来,进而提高反射非对称性人工目标的分类效果。通过使用E-SAR系统在德国DLR附近Oberpfaffenhofen地区获取的L波段PolInSAR影像和国内X-SAR系统在海南陵水地区获取的X波段PolInSAR影像进行实验,证明本文提出的方法能够有效地将与雷达飞行方向不平行的建筑物与森林区分开。     

Change detection method based on fractal model and wavelet transform for multitemporal SAR images

The interaction is quite complex between a ground object and an electromagnetic wave transmitted by synthetic aperture radar (SAR). In a ground resolution cell illuminated by a radar beam, there are many chaotic scatterers and the whole scattering echo has the chaotic characteristics which is usually described with the fractal theory, and the fractal dimension can be used to detect the change information for multitemporal SAR images. In order to improve the change detection effect with fractal model, this paper proposes a new multitemporal SAR image change detection algorithm based on the fractal model and wavelet transform (called FMWT algorithm). The FMWT algorithm has two advantages. One is insensitive to speckle noise; the other is that the change detection accuracy is improved, comparing with a general fractal change detection (GFCD) algorithm. Since the FMWT algorithm adopts the two-dimensional discrete stationary wavelet transform (TDDSWT) technique, it can obtain different direction sub-images and avoid down-sampling of the discrete wavelet transform (DWT). In the paper, not only the simulative data test has been carried out, but also the real natural disaster SAR images have been checked. Experimental results verify that the FMWT algorithm is feasible for multitemporal SAR image change detection, it is not sensitive to speckle noise of SAR images, and the performance of it is better than that of the GFCD algorithm. At the same time, the size of a sliding window will bring some affection in counting fractal dimensions.     

加入多时相纹理的遥感变化检测

基于遥感技术的变化检测是遥感应用的一个重要方面.传统基于遥感的变化检测方法一般是利用光谱信息,较少注意多时相图像间的光谱特征相关关系分析.本文运用地统计学的伪交叉变差函数(Pseudo Cross Variogram)计算多时相图像纹理,定量表达多时相图像间的空间相关关系,并将得到的多时相纹理信息与光谱信息一起用于多时相变化检测.实验结果表明,加入多时相纹理信息可以显著提高变化检测精度,是一种有效的方法.     

融合像素级和对象级的遥感图像变化检测方法

针对现有遥感影像变化检测方法常存在的检测结果破碎、虚检较多、对数据匹配要求高等问题。提出了一种融合像素级和对象级的遥感图像变化检测方法。利用光谱和纹理信息构建单高斯模型,在多尺度上进行像素级变化检测。然后,以像素级检测结果为种子区域,同时在变化前后影像上区域生长,融合生长结果提取变化对象。最后,依据检测需求对变化对象进行特征分类并滤除虚警。实验结果表明,该方法降低了虚检,保持了变化区域的结构完整性,在变化前后图像分辨率存在一定差别时仍有较高的检测精度。     

Automated Image Registration for Hydrologic Change Detection in the Lake-Rich Arctic

Multitemporal remote sensing provides a unique tool to track lake dynamics at the pan-Arctic scale but requires precise registration of thousands of satellite images. This is a challenging task owing to a dearth of stable features to be used as tie points [(TPs), i.e., control points] in the dynamic landscapes. This letter develops an automated method to precisely register images in the lake-rich Arctic. The core premise of the method is that the centers of lakes are generally stable even if their shorelines are not. The proposed procedures first extract lakes in multitemporal satellite images, derive lake centroids and match them between images, and then use the centroids of stable lakes as TPs for image registration. The results show that this approach can achieve subpixel registration accuracy, outcompeting the conventional manual methods in both efficiency and accuracy. The proposed method is fully automated and represents a feasible way to register images for lake change detection at the pan-Arctic scale.      

多时相TM影像相对辐射校正研究

多时相影像对应波段中地物波谱“准”不变特征点(Pseudo-Invariant Features,PIFs)的选取是相对辐射校正的前提,采用目视解译选取主观性较强,在一定程度上影响了校正精度。利用绍兴试验区两时相TM影像,通过样本点间差异阈值,主成份分析和回归分析方法控制选取影像对应波段间的PIFs,使得校正样本点的选取具有客观性。在此基础上获取增益和偏移量并对两时相影像进行相对辐射校正,取得了较好的校正效果,从而有利于土地利用/覆盖变化监测。     

Contextual Spatiospectral Postreconstruction of Cloud-Contaminated Images

A general method has been proposed recently for the contextual reconstruction of cloud-contaminated areas in multitemporal multispectral images. It is based on the idea of making the prediction process learn from information available in the cloud-free neighborhood of contaminated areas. Though promising, this method does not fully exploit all available information, thus leaving room for further methodological enhancements. This letter presents a postreconstruction methodology for improving the contextual reconstruction process by opportunely capturing spatial and spectral correlations characterizing the considered image. In addition, we propose a solution to a problem that has not yet been addressed in the remote sensing literature, i.e., the generation of an error map beside the reconstructed images to provide end-users with helpful indications about reconstruction reliability. Thorough experiments conducted on a multitemporal sequence of Landsat-7 ETM+ images are reported and discussed.     

Automatic change detection for road networks from images based on GIS

Up to now, detailed strategies and algorithms of automatic change detection for road networks based on GIS have not been discussed. This paper discusses two different strategies of automatic change detection for images with low resolution and high resolution using old GIS data, and presents a buffer detection and tracing algorithm for detecting road from low-resolution images and a new profile tracing algorithm for detecting road from high-resolution images. For feature-level change detection (FL-CD), a so-called buffer detection algorithm is proposed to detect changes of features. Some ideas and algorithms of using GIS prior information and some context information such as substructures of road in high-resolution images to assist road detection and extraction are described in detail.     

Automatic Change Detection for Road Networks from Images Based on GIS

Up to now, detailed strategies and algorithms of automatic change detection for road networks based on GIS have not been discussed. This paper discusses two different strategies of automatic change detection for images with low resolution and high resolution using old GIS data, and presents a buffer detection and tracing algorithm for detecting road from low-resolution images and a new profile tracing algorithm for detecting road from high-resolution images. For feature-level change detection (FL-CD), a so-called buffer detection algorithm is proposed to detect changes of features. Some ideas and algorithms of using GIS prior information and some context information such as substructures of road in high-resolution images to assist road detection and extraction are described in detail.     

光谱角—欧氏距离的高光谱图像辐射归一化

辐射归一化旨在减小不同时相遥感影像间因获取条件不一致而导致的非地表辐射变化的差异,是土地覆盖变化监测的重要前提条件。本文根据高光谱图像上同类地物的谱形及数值的相似性,利用光谱角距离(SAD)和欧氏距离(ED)双重判定选取不变特征点,提出了一种基于光谱角—欧氏距离的辐射归一化方法。在评价指标中除了常用的均方根误差和相对偏差,更增加了高光谱特色的衡量光谱保真性指标:皮尔森系数、光谱扭曲程度。利用高光谱遥感CHRIS图像对本文提出方法进行验证,并与基于多元变化检测(MAD)的辐射归一化方法比较。结果表明,本文方法不仅在辐射特性上优于基于多元变化检测(MAD)的方法,而且具有保持光谱特性的优势,具有较好的应用前景。     

Detection of impervious surface change with multitemporal Landsat images in an urban–rural frontier

Mapping and monitoring impervious surface dynamic change in a complex urban-rural frontier with medium or coarse spatial resolution images is a challenge due to the mixed pixel problem and the spectral confusion between impervious surfaces and other non-vegetation land covers. This research selected Lucas do Rio Verde County in Mato Grosso State, Brazil as a case study to improve impervious surface estimation performance by the integrated use of Landsat and QuickBird images and to monitor impervious surface change by analyzing the normalized multitemporal Landsat-derived fractional impervious surfaces. This research demonstrates the importance of two-step calibrations. The first step is to calibrate the Landsat-derived fraction impervious surface values through the established regression model based on the QuickBird-derived impervious surface image in 2008. The second step is to conduct the normalization between the calibrated 2008 impervious surface image with other dates of impervious surface images. This research indicates that the per-pixel based method overestimates the impervious surface area in the urban-rural frontier by 50%-60%. In order to accurately estimate impervious surface area, it is necessary to map the fractional impervious surface image and further calibrate the estimates with high spatial resolution images. Also normalization of the multitemporal fractional impervious surface images is needed to reduce the impacts from different environmental conditions, in order to effectively detect the impervious surface dynamic change in a complex urban-rural frontier. The procedure developed in this paper for mapping and monitoring impervious surface area is especially valuable in urban-rural frontiers where multitemporal Landsat images are difficult to be used for accurately extracting impervious surface features based on traditional per-pixel based classification methods as they cannot effectively handle the mixed pixel problem.     

高分辨率遥感图像港口检测的复合线索视觉注意模型

为提高高分辨率光学遥感图像港口自动检测的准确性,常需综合多类线索并进行复杂的特征提取、融合与分类推理,从而带来较高的计算复杂度。为此,仿生人类视觉注意机制,提出了一种复合线索视觉注意模型,综合利用高分辨率光学遥感图像港口多尺度底层特征和高层知识线索,实现了港口检测特征自然融合与综合分类推理。该方法在提高检测效果的同时较好地控制了计算量的增长,避免了复杂特征的大范围区域提取,采用多步快速算法降低了整个算法的计算复杂度,实现了计算资源受限条件下港口的快速定位与检测。同时,由于能将有限计算资源快速聚焦于最可能含有港口目标的区域,大大提高了目标检测方法响应的实时性。来自不同卫星的高分辨率光学遥感图像实验结果,验证了提出方法的有效性。     

面向对象的高分辨率遥感影像建设用地变化监测

建设用地变化监测对城市可持续发展具有十分重要的意义,为准确提取建设用地的变化信息,本文提出了一种结合旧时期土地利用矢量数据、随机森林及模糊C均值聚类算法的建设用地变化监测方法.首先基于旧时期土地利用矢量数据,将建设用地变化监测分为建成区与非建成区建设用地变化监测;然后进行面向对象分割、基于对象遥感影像光谱、GLCM纹理...     

高分辨率遥感影像场景变化检测的相似度方法北大核心CSCD

针对传统高分辨率遥感影像场景变化检测流程复杂且严重依赖分类结果的问题,本文提出了一种顾及场景全局与局部相似性的变化检测方法。首先,将同一区域两个时相的遥感影像裁切成固定尺寸的图像块,构造场景对图像库,并划分为训练集和测试集;其次,构建融合场景全局与局部相似性的双分支卷积神经网络,实现场景相似度学习;然后,利用训练的相似度学习网络提取训练集场景相似度,并通过阈值遍历的方法得到最佳的相似度阈值;最后,基于相似度阈值将测试集场景对划分为变化场景和未变化场景,得到最终的变化检测结果。试验结果表明,本文方法的总体精度为0.94,Kappa系数为0.88,优于传统的分类后变化检测方法,是一种简单有效的场景变化检测方法。     

联合显著性和多层卷积神经网络的高分影像场景分类

高分辨率遥感影像中的场景信息,对于影像解译和现实世界的理解具有重要意义。传统的场景分类方法多利用中、低层人工特征,但是高分辨率遥感影像的信息丰富,场景构成复杂,需要高层次的特征来表达。本文提出了一种联合显著性和多层卷积神经网络的方法,首先利用显著性采样获取包含影像主要信息的有意义的块,将这些块作为样本集输入卷积神经网络中进行训练,获得不同层次的特征表达,最后联合多层特征利用支持向量机进行分类。两组高分影像场景数据UC Merced 21类和Wuhan 7类试验表明,显著性采样能够有效地获取主要目标,减弱其他无关目标的影响,降低数据冗余;卷积神经网络能够自动学习高层次的特征,相比已有方法,本文方法能够有效提高分类精度。     

利用主成分分析改善土地利用变化的遥感监测精度——以珠江三角洲城市用地扩张为例

近年来,珠江三角洲由于经济的快速发展,城市用地急剧增加,利用多时相的遥感图,可以定量地监测这种城市化的现象。但理,由一般的遥感动态监测方法所得的结果往往夸大变化的程度,以及获得一些不合理的结论．该文提出主成分分析的方法来改善遥感动态监测的精度。将该方法应用应用于珠江三角洲发展最快的东莞市,获得了较满意的结果。     

采用图像差值法提取土地利用变化信息方法—以攀枝花仁和区为例

在遥感、地理信息系统和全球定位系统技术综合应用于山区土地资源动态监测领域的研究工作中，探讨采用以图像差值法为主，分类后比较法为辅提取土地利用信息的基本思路和方法，提出采用定量统计分析的方法来选择合适的差值计算波段和相应的变化阈值。在数据分析过程中，GPS测量数据发挥了重要作用。