一种自适应匹配子空间亚像元目标探测方法

为了克服基于线性混合模型的高光谱遥感影像亚像元目标探测方法的缺陷, 提出了一种基于全限制性线性分解的自适应匹配子空间探测方法。首先利用交叉相关光谱匹配技术求得各个像元所含端元类别信息, 然后根据端元类别信息和全限制性分解的结果构造自适应匹配子空间探测算子, 利用端元类别信息在探测中动态选择端元, 降低端元数目估计偏差对探测结果的影响, 提高探测器对目标与背景的可分性。实验证明, 该方法与其他基于线性混合模型的亚像元目标探测方法相比, 可以更好地克服端元数目估计偏差对探测结果的影响, 无论是端元个数低估还是     

一种基于高斯马尔可夫随机场的异常目标探测方法

利用高斯马尔可夫随机场模型描述像元的邻域相关性信息,并将这种邻域信息引入到局域异常探测器中,提出了一种顾及邻域信息的高光谱遥感影像局域异常目标探测算法。实验证明,该方法克服了传统异常探测方法仅仅利用光谱信息的不足,比经典的RX算法的探测效果更好,并且可以更有效地探测出大于一个像元的异常目标。     

点云中混合像元的探测与剔除算法

针对地面三维激光扫描仪扫描过程中产生混合像元的问题,通过对混合像元产生原理和相关特性的分析,提出了一种用于探测和剔除混合像元的算法,并且编制相应的计算机程序予以实现。同时以球体作为实验扫描对象,用混合像元剔除算法对扫描过程中产生的混合像元予以剔除,并比较了混合像元剔除前后点云拟合模型与实际物体在尺寸上及拟合精度的差异,从而验证了该算法的有效性和实用性。混合像元探测与剔除算法可以为后期进行的点云配准、特征提取和曲面建模等工作提供更精确的点云。     

相似性判别原理在影像变化检测中的应用

针对传统基于Bayes影像变化监测方法中分类的硬划分及对辐射畸变敏感的问题,首先,采用集合相似性测度动态更新变化和未变化两类像元模糊子集,解决变化检测分类过程中硬划分的问题;然后,采用像元相似性测度来判断变化类像元集合和未变化类像元集合中的每个像元是否真正发生了变化,剔除伪变化像元,克服对影像线性辐射畸变敏感的问题,并检测出漏检像元,最终实现影像变化区域的提取。实验结果表明,基于相似性测度的影像变化检测方法可实现变化区域自动化提取,具有较好的应用价值。     

多/高光谱遥感图像的投影和小波融合算法

将高光谱图像与高空间分辨率图像融合后,由于融合图像空间分辨率提高,改变了混合像元内地物组分比例,像元光谱信息较原高光谱图像光谱信息会出现“失真”现象。针对这种情况,考虑混合像元内成分变化进行图像融合,首先利用投影方法模拟多光谱图像得到高光谱图像,并将模拟高光谱图像与原高光谱图像利用小波方法进行融合,融合图像不仅增强了空间信息,而且对光谱信息进行一定的修正,从而提高了环境异常探测等一系列应用的精度。利用Hyperion图像和SPOT-5图像进行融合实验,融合图像能够识别出87.2%目标区域。     

高光谱遥感图像亚像元信息提取方法综述

高光谱遥感图像光谱分辨率高、波谱连续、图谱合一,这为精细地物分类、探测和识别提供了数据基础。然而,由于高光谱遥感图像空间分辨率的局限性及地物场景的复杂分布,混合像元普遍存在于高光谱遥感图像。混合像元是高光谱遥感图像精细信息提取与分析中的难点。解决混合像元问题,实现亚像元级信息的提取与分析是近年来高光谱遥感图像解译的热点和前沿。本文系统梳理了高光谱遥感图像亚像元信息提取的主要研究内容,具体从混合像元分解、亚像元制图及亚像元目标探测3个研究方向综述了经典方法,并对国内外相关方向的研究进展、发展前沿及主要挑战进行了分析与评价,最后分析讨论了高光谱遥感图像亚像元信息提取研究在模型构建、优化求解及与应用结合等方面的研究趋势及方向。     

利用ICA正交子空间投影加权的高光谱影像目标探测算法

提出了一种利用独立成分分析（ICA）正交子空间投影加权的高光谱影像目标探测方法。该方法从影像像元集合的独立成分入手,通过一种光谱相似性测度加权,赋予每个像素合适的权值,从而有效地解决从原始影像中无法正确提取背景数据而造成的虚警概率高的问题。实验结果表明,相比于经典的CEM方法,在相同的探测概率下,该方法能降低1.97%的虚警概率;与相关目标探测算法相比,所提出的算法具有较好的目标探测效果。     

高光谱亚像元定位的线特征探测法

未考虑地物亚像元级空间结构特征是影响高光谱亚像元定位精度的因素之一。为了有效解决这一问题,本文提出一种基于混合像元线特征探测的亚像元定位算法。首先,通过光谱解混确定含典型线状地物的混合像元。然后,基于完备直线集的最大线性指数方法确定其余含线特征的混合像元,使用模板匹配方法结合像元引力确定含线特征混合像元的亚像元类别。最后,基于线性优化的方法迭代确定剩余混合像元的亚像元类别。通过真实数据及仿真数据的试验,结果表明所提出的方法能有效提高亚像元定位精度。     

一种快速角点探测算子研究

基于SUSAN和角点几何结构分析的思想提出一种改进的快速角点探测算子.首先进行角点粗探测,然后再对粗探测的结果进行角点几何结构分析,即图像中的像元如果为角点,它必须满足角点的圈层结构.通常认为角点处各个方向上灰度差变化都较大,所以角点响应函数采用反映角点突出性的响应函数,使提取的角点含有更高的信息量.实验证明,本算法提取的角点定位准确,快速有效,可以适用于实时图像处理.     

基于混合像元分解技术的植被信息抑制研究与应用

植被覆盖直接影响地质信息提取的精度,是遥感地质中的难点。利用混合像元分解方法可从像元中剥离出植被成分,进而获得相对纯粹的地质信息,因此具有抑制植被信息的作用。利用混合像元分解方法分解图像的植被像元组分,利用归一化植被指数变化情况描述植被抑制效果,并结合主成分分析法对实验区域进行地层界线划分。结果表明,该方法可有效划分地层界线。     

Support Vector Machines for Automatic Multi-class Change Detection in Algerian Capital Using Landsat TM Imagery

Recently, Support Vector Machines (SVMs) have shown a practical relevance in various image processing applications. This paper investigates their applicability for land cover and land use change detection using multi-sensor images of remote sensing. Then, the most widely used approaches for multi-class SVMs, which are the One-Against-All and the One-Against-One with both Max-Win and DDAG decision rules are implemented to perform multi-class change detection. SVMs are evaluated in comparison with artificial neural networks using different accuracy indicators. The results obtained showed that SVMs are much more efficient than artificial neural networks and highlighted their suitability for land cover change detection.     

多时相遥感影像变化检测的现状与展望

多时相遥感影像变化检测技术能够监测生态环境变化、跟踪城市发展,对于研究人类与自然环境之间的交互关系有着重要的意义。随着新型遥感影像的不断普及,变化检测也在高光谱影像变化检测和高分辨率影像变化检测两个方向上有了深入的探索。本文围绕变化检测的基本流程,从预处理、变化检测方法、阈值分割与精度评价4个角度介绍了变化检测研究的最新进展,并总结了变化检测技术的主要应用领域。最后,对变化检测技术未来的发展进行了展望。     

Remote sensing research issues of the National Land Use Change Program of China

In 1999, the Ministry of Land and Resources (MLR) of China launched the National Land Use Change Program especially to monitor the scale and distribution of urban expansion and the decrease in cultivated land through remote sensing technology. This Program has been carried out annually and continuously for seven years since then and played an important role in the policy-making of MLR about land management and planning. This paper gives an overview about this Program and discusses several research issues. First, the remote sensing data sources and other ancillary data used in this Program are presented. The approaches for image preprocessing, i.e. radiometric normalization, image geometric rectification and image fusion are then introduced with an emphasis on the algorithm development for image registration. Second, land use change detection technique is the most critical and complex aspect of the Program. The methodologies for change detection using either bi-temporal image pair or one existing land use map and one remotely sensed image are detailed. Third, since the data of land use changes derived from remote sensing will be operationally used for local and central government, field validation and accuracy assessment are crucial to ensure the reliability of change detection results. The strategy of field work and the resulting accuracy evaluations is presented. The land use and change information derived from remotely sensed data has wide applications for land management, including land use database updating, verification of land use planning and monitoring of national high-tech parks. Last, suggestions on how to make full use of the images and change detection result, to improve the consistency of land use classification and to develop change detection algorithms for diverse and complex remote sensing data are given.     

论地理国情监测的技术支撑

针对地理国情监测中大幅面多时相遥感影像变化检测的需求,提出了一种基于卡方变换和样本选择的面向对象遥感影像变化检测方法。首先对多时相遥感影像进行多尺度分割获取像斑;然后,提取像斑的多维特征,采用基于卡方变换的特征融合方法计算像斑的加权差异度;最后,自适应选择训练样本,通过基于最大期望算法的贝叶斯阈值确定方法获取变化阈值,并对加权差异影像进行二值分割获取变化检测结果。以武汉市东湖高新技术开发区为例,利用多时相高分辨率遥感影像进行土地覆盖变化检测。试验结果表明,该方法可以克服全样本变化向量分析法及全样本卡方变换检测法难以满足阈值确定条件的不足,获得更准确的变化阈值,保证变化检测正确率高而又有效地降低漏检率,从而获得更好的变化检测结果,在地理国情监测中具有一定的应用价值。     

遥感影像变化检测在地形图快速修测中的应用

首先介绍地形网修测中常规判读地物地貌发生变化的方法,以及将变化检测技术应用于地形图修测中的必要性;其次介绍变化检测的基本原理、发展历史、国内外对变化检测概念的不同定义以及变化检测技术的特点和应用范围;紧接着从地形图快速修测这一客观需要出发,简明扼要地叙述变化检测方法的选择、变化检测的一般过程、变化检测结果在地形图修测中的应用,并结合变化检测的精度评估公式分析将变化检测技术应用于地形图修测工作中的实际效率;最后展望变化检测技术在地形图快速修测中的应用前景。     

Detecting and Enumerating New Building Structures Utilizing Very‐High Resolution Imaged Data and Image Processing

Abstract

Information on the number and type of new building structures is required by urban and transportation planners and the real estate industry. The goal of this paper is to explore the potential of high resolution imagery for meeting public and private sector demands for information on new buildings. The value of 1 m, 5 m, and 10 m panchromatic and 1 m color scanned aerial photography images acquired in 1997 and 1998 for a study area within the City of San Diego, California is assessed for general change detection and building enumeration. Both semi‐automated and interactive change‐detection approaches are evaluated. We demonstrate that interactive, visual‐based approaches appear to be the most accurate (within 1% of actual count) and efficient approach for generating information on the number of new buildings associated with single family residential land use. More automated approaches to detecting and enumerating image microfeatures may be useful as enhancements for visual‐based assessments and may be practical in areas composed mostly of large buildings associated with commercial and industrial land use. The highest accuracy for automated approaches was an undercounting of 11% for residential buildings and overcounting of 20% for those associated with commercial and industrial land use.     

基于时空自相关的建筑物变化检测

针对传统基于像素级的变化检测方法在变化分析中难以利用像元间的时空关系、变化检测结果精度低的问题,提出了一种基于时空自相关的建筑物变化检测方法。首先,利用形态学建筑指数(Morphological Building Index,MBI)进行建筑物提取,并通过长宽比、面积等剔除道路信息优化建筑物提取;其次,采用时空自相关模型分别构建两期MBI特征影像的时空自相关性指标值作为对应像元的相似性测度;最后,利用最大类间方差(otsu)法确定最优阈值,得到变化检测结果。实验表明,该方法所得变化检测结果更完整,漏检率和误检率均低于对比算法,该方法基本满足变化检测需求,为高分影像建筑物变化检测提供一种新的技术手段。     

Multi-source change detection with PALSAR data in the Southern of Pará state in the Brazilian Amazon

Optical data is broadly used for change detection studies, despite being hindered by atmospheric conditions. Synthetic Aperture Radar (SAR) data can be useful for change detection in areas with frequent cloud coverage as SAR systems are capable of obtaining images almost independently from atmospheric conditions. This study aims to verify the difference in results of using SAR data instead of optical data for change detection purposes. Different levels of one hierarchical legend and both pixel and region-based classifiers were used. Change results were evaluated considering the use of rectangular matrices to incorporate the occurrence of impossible changes and relative comparison between change maps. Although the change maps obtained using only optical data were more accurate than those using either one or two land cover classifications based on L-band SAR data, the difference in the accuracy of change maps decreases with the use of less detailed legends. Additionally, results indicate that L-band SAR and multi-sensor approaches are adequate for deforestation identification even if post-classification results did not achieve global accuracy values superior to 0.86. The most accurate change detection results obtained in this work were not associated with the overall accuracy of land cover classifications, but with the distribution and accuracy of specific land cover classes.     

利用遥感影像进行变化检测

首先分析了利用影像进行变化检测的紧迫要求和存在的困难 ,接着对变化检测的方法进行分类 ,提出将影像配准与变化检测整体同步解求的思想和在此基础上进行三维变化检测的可能性 ,并在文中概要介绍和比较了各种变化检测的 7种方法 ,最后展望了下一步应继续研究的方向。     

Automatic change detection of geo-spatial data from imagery

The problems and diffi-culty of current change detection tech-niques are presented. Then, according to whether image registration is done before change detection algorithms,the authors classify the change detec-tion into two categories:the change de-tection after image registration and the change detection simultaneous with image registration. For the former,four topics including the change detec-tion between new image and old im-age, the change detection between new image and old map, the change detec-tion between new image/old image and old map, and the change detection be-tween new multi-source images and old map/image are introduced. For the latter, three categories, i. e. the change detection between old DEM,DOM and new non-rectification image,the change detection between old DLG, DRG and new non-rectification image, and the 3D change detection between old 4D products and new multi-overlapped photos, are dis-cussed.