Polarimetric SAR Data Correction and Terrain Topography Measurement Based on the Radar Target Orientation Angle

Topographic variations caused by the range and the azimuth terrain slopes induce polarization orientation changes which cause the polarization to rotate about the line of sight. The existence of these variations reduce the accuracy measurement of geophysical parameters from polarimetric synthetic aperture radar (PolSAR) images. For this reason most inversion studies are best done in area of flat earth. In area which has significant terrain variations require compensation for topography. In real situations, terrain slopes rotate the polarization basis of the polarimetric scattering matrices by an orientation angle shift, and induce significant cross-polarization power. In this paper, two methods have been investigated using the polarimetric orientation angle (PAO): the first one involves the rotation of the polarimetric scattering and coherency matrices to achieve maximum azimuthally asymmetry for polarimetric data compensation to ensure accurate estimation of geophysical parameters in rugged terrain areas. The second approach has been developed which measures azimuth and range terrain slopes that are related to shifts in polarization orientation angle. Terrain elevation maps relative to a plane parallel to the radar line of sight can then be generated by integrating these slopes requiring only one PolSAR flight pass by combing orientation angle estimation and a shape-from-shading technique (SFS) which is mostly used by the computer vision community. Experimental results with C-band polarimetric RADARSAT2 data are used evaluate the data compensation algorithm and DEM generation.     

极化方位角补偿信息支持下的植被参数反演

SAR图像中散射目标的散射矩阵受极化方位角(POA)的影响会改变散射体的散射特性,散射矩阵是极化干涉SAR (PolInSAR)估计不同极化状态下复相干性的基础。本文根据极化方位角产生机制,建立了多视情况下基于极化方位角补偿的极化干涉相干性估计模型,分析了极化方位角补偿对相干性估计方法和不同散射机制下相干性估计的影响程度,研究了基于三阶段法与极化方位角补偿的植被参数反演。利用L波段SIRC全极化SAR图像为实验数据验证极化方位角补偿对极化干涉相干性估计和植被参数反演的可行性。实验结果表明,极化方位角补偿能够改变不同极化状态相干性分布规律,提高相干直线拟合精度,改善植被参数反演的可靠性和合理性。     

SARBuD 1.0： 面向深度学习的GF-3 精细模式SAR建筑数据集

合成孔径雷达SAR（Synthetic Aperture Radar）是开展城市建筑区信息获取与动态监测的重要数据源。本文建立了一个面向深度学习建筑区提取的中高分辨率SAR建筑区数据集SARBuD1.0 （SAR BUilding Dataset）。该数据集包含了覆盖中国不同区域的27景高分三号（GF-3）精细模式SAR图像,并从中获取了建筑区共计60000个SAR样本数据,结合光学图像与专家解译,制作了与样本数据对应的标签图像。SARBuD1.0数据集包含了不同地形场景类型、不同分布类型、不同区域的建筑区。该数据集可支持研究者对建筑区进行图像特征分析、辅助图像理解,并可对当前热点深度学习方法提供训练、测试数据支持。本文以山区建筑为例,使用传统纹理特征与深度学习特征对建筑区进行了特征分析与比较,相比于传统的人工设计的纹理特征,卷积神经网络具有更深、更多的特征,利用网络模型浅层的不同卷积核采样可得到各种纹理特征,在网络的深层卷积结构中可获取代表着类别的深层语义特征,使得分类器能更好地检测并提取图像中指定的目标。基于本数据集利用深度学习方法对不同地形区域的建筑区进行提取实验。实验结果表明基于本数据集训练的深度学习模型,对建筑区提取可以取得良好的结果,说明该数据集可以很好支持面向大数据的深度学习方法。其他学者可以基于SARBuD1.0数据集开展建筑区图像特征分析与语义分割提取等方面的研究。     

面向城市建成区扩展模拟的CA-Markov模型改进

对城市建成区扩展的预测是防止城市蔓延的重要管理依据。目前,元胞自动机-马尔可夫链模型,已成为城市建成区扩展预测的重要方法。该模型对指标权重的赋值方法较为敏感,以往的单一指标赋值法,影响了城市建成区扩展预测的精度和可信度。为此,本研究提出整合传统权重赋值法的AHP和逻辑回归模型改进CA-Markov模型。研究选择云南省大理市为案例,对2020、2030年的城市建成区扩展进行模拟和预测,最后进行精度验证。研究结果表明：①Kappa指数可达到96.8%,预测结果有较好的一致性。②大理市的城市建成区扩展均表现为继续向外扩展,以东南、西北方向和两片建成区之间为主要扩展方向。研究提供了组合权重赋值法改进CA-Markov模型,这将为规划者在未来规划中提供强有力的支持。     

Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data

An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study. The Moderate Resolution Imaging Spectroradiometer (MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite (VIIRS)-based nighttime light (NTL) data for robust extraction and mapping of urban built-up areas. The MODIS-based newly proposed Urban Built-up Index (UBI) was combined with NTL data, and the resulting Enhanced UBI (EUBI) was used as a single master image for global extraction of urban built-up areas. Due to higher variation of the EUBI with respect to geographical regions, a region-specific threshold approach was used to extract urban built-up areas. This research provided 500-m-resolution global urban built-up map of year 2014. The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States. The comparative analysis demonstrated finer details of the urban built-up cover estimated by the resultant map.     

基于长时间序列遥感数据的太原市建成区近50年扩张规律分析

本文基于太原市1973-2020年遥感影像,利用人工目视解译结合Google历史影像校正提取建成区,引入紧凑度、扩张强度、扩张速度及重心迁移模型等指标对太原市建成区近50年的时空演变特征进行深入分析,并分析太原市建成区扩张的主要驱动力因素。研究表明：①1973-2020年太原市建成区面积扩张了15.12倍,平均年增长面积为7.85 km²;建成区紧凑度普遍较低,在南北方向扩展明显,重心沿南西约30°方向迁移了约3500 m。②引起太原市建成区扩张的主要因素有经济、人口扩张等,其中国民生产总值与人均GDP起到关键性作用。     

基于夜光遥感数据的中原城市群建成区时空变化分析

本文基于“类NPP-VIIRS”夜光遥感数据集,采用改进的统计数据比较法提取中原城市群建成区,利用重心迁移指标和典型景观格局指标,分析中原城市群建成区2002—2020年的时空变化特征。研究表明:①中原城市群建成区扩张强度先快后慢,整体呈下降趋势;建成区重心在经历多次偏移后最终指向东南方向,但一直位于郑州大都市区范围内。②中原城市群发展迅速,建成区总面积在2002—2020年增加了1.429倍;在2011—2012年出现大量新兴城镇;2014年之后趋于稳定,城镇之间的联系越来越紧密。③中原城市群建成区空间格局复杂度逐年上升,破碎程度总体降低,郑州大都市区建成区扩张速度明显快于中原城市群整体建成区扩张速度。     

一种高分影像建筑区分块表示与合并提取方法

建筑区是一种重要的人工地理要素,利用高分辨率卫星影像可以在更精细的尺度上获取建筑区信息。针对建筑区这类结构复杂、面积相对较大的地物类,提出一种分块表示与合并提取方法。首先,通过角点上下文约束来划分图像,并将获得的图像块作为影像处理的基本单元;然后,利用空间变异函数来建模每个图像块并提取特征描述参数,进一步通过主成分变换实现建筑区图像块的结构特征表示;最后,根据图像块空间结构特征的相似性实现建筑区的判别。实验结果表明,该方法能够有效实现高分影像建筑区的提取,并且对不同分辨率的高分影像表现出良好的适应性。     

遥感影像与POI数据相结合的城市建成区提取适用性研究——以沈阳市为例

城市建成区的发展状况是地理国情监测的重要内容,本文基于遥感影像数据和POI数据对城市建成区进行提取,针对二者的适用性问题进行了研究。试验以沈阳市为研究区域,在研究区域内选择2016年遥感影像数据和POI数据作为数据源进行对比分析。首先,对遥感影像数据和POI数据进行预处理;其次,通过监督分类的方法对遥感影像进行建成区的提取;然后,采用核密度估计法分析POI数据并提取出建成区;最后,利用叠加分析法对比分析这两种数据的适用性。试验结果表明：使用遥感影像数据作为数据源可以较为全面客观地反映城市建成区的发展现状;利用POI数据提取出的城市建成区具有较强的经济属性,能够很好地反映出城市中的经济活跃区。     

全极化SAR数据反演桥面高度

根据高分辨率SAR图像上建筑区的影像特征, 提出了基于灰度共生矩阵(gray-level cooccurrence Matrix, GLCM)纹理分析的建筑区提取方法, 该方法由初步定位和边界调整2个步骤组成, 均遵循特征计算、基于Bhattacharyya距离的特征选择和KNN分类流程, 所不同的是2个步骤中分别采用了逐块和逐点计算纹理特征的方式以兼顾纹理分析的效率和准确性。文中对不同SAR传感器获取的图像进行了实验。实验结果表明, 选用具有最大Bhattacharyya距离值的3或4个特征可以获得较好的初步定位结果, 建筑区的检测率超过80%, 虚警率低于10%;随着边界调整的进行, 检测到的建筑区边界逐渐接近于真实边界。实验结果验证了该算法的有效性。     

1980—2015年中国建设用地变化研究

土地特别是建设用地的空间格局与演变是城镇研究的热点问题。本文利用1980、1990、1995、2000、2005、2010、2015年7期的中国土地利用遥感数据,对1980—2015年中国建设用地变化进行了系统性的分析。研究工作主要有:①计算1980—2015年中国的城镇用地、农村居民点、其他建设用地及总建设用地的面积和增长率,从而得到1980—2015年中国的建设用地变化速率。②运用GIS软件中的联合分析工具,得到1980—2015年中国建设用地空间变化格局。③制作土地利用转移矩阵,从而得到1980—2015年中国建设用地结构变化情况。研究表明,中国建设用地整体上表现为持续扩张的态势;东部及沿海地区增长速度较快,青藏高原基本无变化,中部及东北地区增长速度较缓,西北地区有少量增加;增加建设用地以耕地转入为主。     

利用GLCM纹理分析的高分辨率SAR图像建筑区检测

根据高分辨率SAR图像上建筑区的影像特征, 提出了基于灰度共生矩阵(gray-level cooccurrence Matrix, GLCM)纹理分析的建筑区提取方法, 该方法由初步定位和边界调整2个步骤组成, 均遵循特征计算、基于Bhattacharyya距离的特征选择和KNN分类流程, 所不同的是2个步骤中分别采用了逐块和逐点计算纹理特征的方式以兼顾纹理分析的效率和准确性。文中对不同SAR传感器获取的图像进行了实验。实验结果表明, 选用具有最大Bhattacharyya距离值的3或4个特征可以获得较好的初步定位结果, 建筑区的检测率超过80%, 虚警率低于10%;随着边界调整的进行, 检测到的建筑区边界逐渐接近于真实边界。实验结果验证了该算法的有效性。     

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Abstract

Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as built-up land features extraction index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, vegetation and water. The histogram overlap method and the spectral discrimination index (SDI) are used to study separability. BLFEI index uses the two bands of infrared shortwaves, the red and green bands of the visible spectrum. OLI imagery of Algiers, Algeria, was used to extract built-up areas through BLFEI and some new previously developed built-up indices used for comparison. The water areas are masked out leading to Otsu’s thresholding algorithm to automatically find the optimal value for extracting built-up land from waterless regions. BLFEI, the new index improved the separability by 25% and the accuracy by 5%.     

利用高分影像和珞珈一号数据进行住房空置率高精度空间估算

本文主要利用珞珈一号夜间灯光数据和高分遥感影像数据,提出了一种对研究区按建成区高层区、建成区低层区和非建成区住房进行空置率分区块估算的方法,采用夜晚实地记录的方法对估算结果进行精度检验,并通过LISA聚集图分析其空间聚集情况.表明结果:①研究区整体住房空置率为17.88％,均方根误差为0.14,其中非建成区的住房空置率...     

利用改进的归一化差异水体指数（MNDWI）提取水体信息的研究

在对M cfeeters提出的归一化差异水体指数(NDWI)分析的基础上,对构成该指数的波长组合进行了修改,提出了改进的归一化差异水体指数MNDWI(M odified NDWI),并分别将该指数在含不同水体类型的遥感影像进行了实验,大部分获得了比NDWI好的效果,特别是提取城镇范围内的水体。NDWI指数影像因往往混有城镇建筑用地信息而使得提取的水体范围和面积有所扩大。实验还发现MNDWI比NDWI更能够揭示水体微细特征,如悬浮沉积物的分布、水质的变化。另外,MNDWI可以很容易地区分阴影和水体,解决了水体提取中难于消除阴影的难题。     

Monitoring growth of built-up areas in indo-gangetic plain using multi-sensor remote sensing data

This paper provides an approach for rapid and accurate estimation of built-up areas on a per pixel-basis using a integration of two coarse spatial resolution remote sensing data namely DMSP-OLS and MODIS NDVI. The DMSP-OLS data due to its free availability, high temporal resolution and wide swath was used for regional level mapping of built-up areas. However, due to its low radiometric resolution, the built-up areas cannot be estimated accurately from the DMSP-OLS data. In present study, the DMSP-OLS data was combined with MODIS NDVI data to develop an Human Settlement Index (HSI) image, which estimated the fraction of built-up area on a per pixel basis. The resultant HSI image conveys more information than both the individual datasets. These temporal HSI images were then used for monitoring urban growth in Indo-Gangetic plains during the 2001–2007 time period. Thus, the present research can be very useful for regional level monitoring of built-up areas from coarse resolution data within limited time and minimal cost.     

Spatio-temporal analysis for monitoring urban growth – a case study of Indore City

Urban sprawl is characterized by haphazard patchwork of development, which leads to an improper development in any city. To prevent this kind of sprawl in future, it is necessary to monitor the growth of the city. Hence, an attempt has been made in the present study to monitor the urban growth over a period of time by employing Remote Sensing and Geographic Information System techniques in conjunction with Shannon entropy. Shannon entropy is a measure to determine the compactness or dispersion of built-up land growth in the urban areas. The growth patterns of urban built-up land have been studied initially by dividing the area into four zones. The observations have been made with respect to each zone. Then, the study area has been divided into concentric circles of 1 km buffers and the growth patterns have been studied based on urban built-up density with respect to each circular buffer in all four zones. These observations have been integrated with road network to check the influence of infrastructure on haphazard urban growth. It has been found from the study that Shannon entropy is a good measure to determine the spatial concentration or dispersion of built-up land in the city. The study also proved the potential of RS and GIS techniques in the spatio-temporal analysis of urban growth trends and their consequences in the lands adjoining to urban areas.     

Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories. The water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear together in processes for identifying problems and analyzing, synthesizng, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning.     

An improved building detection approach using L-band POLSAR two-dimensional time-frequency decomposition over oriented built-up areas

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.     

城市道路的多特征多核SVM提取方法

针对高分辨率遥感影像中城市道路提取的复杂性及SVM的分类性能,提出了一种城市道路的多特征多核SVM提取方法。首先利用FCM算法将原始影像粗分为建成区和非建成区两类,剔除非建成区;然后根据分水岭分割算法分割建成区并提取分割对象的光谱特征与空间特征,以全局核函数和局部核函数加权组合的方式构建多核SVM对建成区进行二次分类,去除建成区中的建筑物等非道路信息;最后利用数学形态学处理,获得最终的道路提取结果。试验结果表明：文中所提方法能够较精确地提取城市道路信息,分类精度高于单核SVM提取及其他对比方法。