基于LightGBM和多光谱—SAR多特征综合的中亚干旱区城市不透水面提取

不透水面是评价城市化水平和城市生态环境的重要指标,是近年来城市遥感研究中的热点方向之一。与湿润、半湿润区相比,干旱区城市植被覆盖度较低,不透水面与裸土、荒漠之间相似的光谱特征导致传统基于光学影像的亚像元分解法与光谱指数法在干旱区不透水面提取的适用性降低。针对该问题,提出一种多光谱与合成孔径雷达（SAR）影像多特征综合的方法以增大不透水面与其他地物覆盖类型之间的特征差异,从而提取干旱区城市不透水面。以阿斯塔纳、塔什干和杜尚别3个中亚城市为研究区,哨兵2号和哨兵1号影像为数据源,通过LightGBM算法对多光谱和SAR图像的空间特征、SAR的极化特征进行分类并提取不透水面。研究对比了不同特征组合以及不同分类方法的不透水面提取结果,实验结果表明：多光谱与SAR影像多特征综合的方法能有效提高干旱区不透水面提取精度,明显改善干旱区其他土地覆盖类型错分为不透水面的问题;LightGBM算法与XGBoost、HistGBT等基于梯度提升决策树的算法和随机森林等方法相比能获取更高的精度,更适用于干旱区不透水面提取。这表明基于LightGBM以及多光谱和SAR多特征联合的方法能够有效提取中亚干旱区城市不...     

基于改进全卷积神经网络的高分遥感影像不透水面提取制图

针对现阶段高分辨率遥感影像提取城市不透水面的方法普遍精度不高的现状,以国产高分二号(GF-2)遥感影像为数据源,基于局部注意力机制的密集连接全卷积神经网络模型,以天津市遥感影像为例,构建不透水面样本库及训练不透水面提取模型,用测试影像进行测试并采用多种精度评价方法与传统的不透水面提取算法相对比。结果表明,本文方法在遥感不透水面提取方面具有更好的完整性,其像元精度(PA)、均交并比(MIoU)、综合评价指标F1和Kappa系数分别为0.883 2、0.736 4、0.848 2和0.753 3,均高于决策树分类算法、支持向量机法、随机森林算法。此外,本文方法具有较好的泛化性,在遥感影像不透水面提取上具有较强的应用价值。     

基于多尺度特征融合的U-Net网络高分影像不透水面提取研究

不透水面作为监测城市生态环境的重要指标,其信息提取具有重要意义。由于城市地表的复杂性及细化的城市管理需要,急需提取高精度的城市不透水面。但是基于传统方法提取高精度的城市不透水面面临巨大困难。而深度学习方法因其自动化提取影像特征的特点逐渐成为遥感影像地物提取的新兴方法。基于此,采用多尺度特征融合的U-Net深度学习方法以提升语义分割精度,开展高分辨率遥感影像不透水面的精确提取研究。模型引入残差模块代替普通卷积以加深网络,提取更多影像特征;加入金字塔池化模块增强网络对复杂场景的解析能力;利用跳跃连接方式融合不同尺度特征,有利于恢复空间信息。以广州市航摄正射影像为数据源,通过卷积神经网络将遥感影像分割为背景、其他、植被、道路和房屋5种地物类型,将其与人工目视解译的地面真值进行验证,最终提取研究区域不透水面。实验证明：多尺度特征融合的U-Net模型总体精度和Kappa系数分别为87.596% 和0.82。在定性与定量两个方面均优于传统的监督分类法、面向对象分类法和经典U-Net模型法。结果表明：该模型利用多维度影像特征信息,有效提升了复杂场景图像的分割精度,分割效果好,适用于高分辨率遥感影像不透水面提取,该研究成果可为城市环境监测提供数据支撑。     

协同Sentinel合成孔径雷达和光学影像多特征的不透水面随机森林提取方法

不透水面的精确提取对区域人口密度估计、环境评估、灾害预测、水文模型构建、城市热岛效应研究以及气候变化分析等具有重要意义。传统大尺度不透水面提取方法主要受限于遥感数据质量和提取特征的选择，提取的不透水面空间分辨率较低，难以满足现阶段不透水面的精细化需求。以Sentinel-1 SAR和Sentinel-2 MSI为遥感数据源，从光谱、纹理、时序等3个维度选取不透水面的多个提取特征，构建了基于随机森林的不透水面提取模型，并利用GEE平台开展了2020年长三角地区的10 m空间分辨率不透水面提取实验。结果表明：在不同类型实验区，与仅用光谱特征、光谱特征和时序特征相比，该方法的总体精度、Kappa系数分别提升5%、9%和2%、6%，且针对不透水面覆盖水平不同的各类城市，均具有较好的提取效果；长三角地区全域尺度不透水面提取的总体精度和Kappa系数分别达93.75%和0.88，不透水面面积为61 591.38 km²，占全域总面积的比例约为17%，主要分布在长三角的东部区域，西、北部不透水面占比较低且呈放射性分布。该方法针对10 m分辨率遥感影像提出的，适用于山区、乡村、城...     

融合WorldView-2与机载激光雷达数据估算城市不透水面

针对高空间分辨率遥感影像进行城市不透水面提取时存在的同物异谱、异物同谱及阴影等局限性,提出一种基于WorldView-2高分影像与机载激光雷达数据融合的分层分类估算城市不透水面的方法。该方法首先运用基于雾霾与比值(haze-and-ratio-based,HR)的融合算法对WorldView-2多光谱波段与全色波段进行数据融合;然后依据LiDAR归一化数字表面模型(normalization digital surface model,nDSM)高度阈值分为地面物体与非地面物体,运用像元尺度上分层支持向量机分类算法进行城市不透水面百分比估算;最后结合特征阈值和GIS空间分析法探测阴影区域不透水面。研究结果表明,与传统的高分影像提取城市不透水面方法相比,该方法可以明显改善材质复杂的建筑物屋顶提取不完整,以及高亮裸土与高反照度屋顶相互混淆的现象,并通过阴影校正可以较好地区分阴影区域的植被与不透水面信息,进而提高城市不透水面估算精度。     

福州城区不透水面的光谱混合分析与识别制图

作为Ridd V-I-S模型中的一个重要组成部分,城市不透水面在监测城市扩展和解释人类活动对生态环境的影响起着非常重要的作用。利用图像处理技术,可以迅速地从遥感图像中提取城市不透水面信息。本文以福州城区为例,利用最小噪音分量变换法研究Landsat ETM 影像中城市不透水面信息的提取。通过选取最小噪音分量变换后的前3个分量和线性光谱混合模型,测算得到了高反照率、低反照率、植被及土壤4个模拟城市不同土地覆盖类型的终端地类分量。通过综合低反照率和高反照率两个终端地类,最后得到了不透水面分量。结果表明,城市不透水面的增加对城市生态环境有负面影响。     

基于混合光谱分解的兰州城市热岛与下垫面空间关系分析

利用Landsat ETM+数据,采用混合像元线性光谱分解方法提取的城市植被覆盖度与不透水面表征城市下垫面,通过单窗算法反演地表真实温度,对兰州市中心城区的夏季城市热岛强度与城市下垫面的空间分布关系进行相关分析。结果显示,利用中等分辨率ETM+影像对兰州中心城区不透水面和植被盖度分布提取,其成本较低,精度令人满意;兰州城区植被覆盖、不透水面与热岛强度的分布呈空间正自相关,地表温度的空间依赖性极强,与植被盖度和不透水面在空间方向上的相关性差异较大。     

城市不透水面及其与城市热岛的关系研究-以泉州市区为例

利用Landsat TM卫星影像提取了泉州市1989到1996年的城市建成区不透水面,并研究了其与城市热岛之间的关系。根据Ridd(1995)提出的城市建成区不透水面与植被覆盖度有很强的负相关关系的思想,先利用归一化植被指数求出泉州市建成区的植被覆盖度,进而提取了泉州市建成区的不透水面。通过比较所提取的两个时相不透水面信息,可以看出泉州市区不透水面的面积在7年里有了明显的增加,并主要沿研究区东南部扩展。通过将所提取的不透水面信息与利用TM6波段反演的地表温度进行相关分析,可发现二者之间存在着明显的正相关关系。     

多时相影像福州市不透水面对城市地表温度的空间分布研究

随着城市化进程的加快,城市热力场也随之发生变化,从而影响着城市区域环境、社会经济以及社会环境。由于NDVI具有季相变化的不稳定性,本研究采用两个时相TM/ETM+影像分析福州市及其周边地区不透水面对热力场的时空分布变化状况。为了获取精确的城市不透水面信息,本实验采用NDVI二元法结合2000年同区域的IKONOS影像提取不透水面信息。通过定量分析不透水面百分比、NDVI与地表温度的关系,得出不透水面百分比与城市地表温度呈线性相关,其相关系数在0.7左右;尤其30%以上的不透水面对地表热环境的空间分布影响最为突出,因此,相对于不稳定的NDVI而言,不透水面信息能更好地反映城市热环境的空间分布状况。     

通过不透水面聚集密度法提取城市建成区

针对已有基于不透水面提取城市建成区的方法无法保证建成区连续性的问题,提出了一种基于对象不透水面聚集密度的城市建成区提取方法。该方法计算了不透水面聚集密度并进行分级,提取聚集程度高的区域为输入对象,并将被建成区完全包围的绿地水体也包含在内;利用长时序遥感影像建成区提取结果,使用城市空间形态扩展指标对济南市1997—2017年城市时空演化特征进行分析。结果表明,该方法提取精度达到93.5%,能准确刻画出城市形态,保持建成区的完整性;1997—2017年间,济南市建成区面积不断扩张,扩张速度与强度分别达到10.48km~2/a、0.32%,呈现带状轴向扩展模式;1997—2013年,建成区扩展的不规则程度在逐年减少,2013年以后建成区形状变得复杂。     

基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用

混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。     

Estimating impervious surfaces from medium spatial resolution imagery: a comparison between fuzzy classification and LSMA

Impervious surfaces are important environmental indicators and are related to many environmental issues, such as water quality, stream health and the urban heat island effect. Therefore, detailed impervious surface information is crucial for urban planning and environment management. To extract impervious surfaces from remote sensing imagery, many algorithms and techniques have been developed. However, there are still debates over the strengths and limitations of linear versus nonlinear algorithms in handling mixed pixels in the urban landscapes. In the meantime, although many previous studies have compared various techniques, few comparisons were made between linear and nonlinear techniques. The objective of this study is to compare the performance between nonlinear and linear methods for impervious surface extraction from medium spatial resolution imagery. A linear spectral mixture analysis (LSMA) and a fuzzy classifier were applied to three Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images acquired on 5 April 2004, 16 June 2001 and 3 October 2000, which covered Marion County, Indiana, United States. An aerial photo of Marion County with a spatial resolution of 0.14 m was used for validation of estimation results. Six impervious surface maps were yielded, and an accuracy assessment was performed. The root mean square error (RMSE), the mean average error (MAE), and the coefficient of determination (R ²) were calculated to indicate the accuracy of impervious surface maps. The results show that the fuzzy classification outperformed LSMA in impervious surface estimation in all seasons. For the June image, LSMA yielded a result with an RMSE of 13.2%, while the fuzzy classifier yielded an RMSE of 12.4%. For the April image, LSMA yielded an accuracy of 21.1% and the fuzzy classifier yielded 17.0%. For the October image, LSMA yielded a result with an RMSE of 19.8%, but the fuzzy classifier yielded an RMSE of 17.5%. Moreover, a subset image of the commercial, high-density and low-density residential areas was selected in order to compare the effectiveness of the developed algorithms for estimating impervious surfaces of different land use types. The result shows that the fuzzy classification was more effective than LSMA in both high-density and low-density residential areas. These areas prevailed with mixed pixels in the medium resolution imagery, such as ASTER. The results from the tested commercial area had a very high RMSE value due to the prevalence of shade in the area. It is suggested that the fuzzy classifier based on the nonlinear assumption can handle mixed pixels more effectively than LSMA.     

结合像元形状特征分割的高分辨率影像面向对象分类

针对高分辨率遥感影像空间分辨率高,结构形状、纹理、细节信息丰富等特点,提出一种新的融合特征的面向对象影像分类方法来提取城市空间信息。基本过程包含以下4个方面:①提取影像的几何纹理等结构;②融合几何与纹理特征的面向对象影像分割;③提取对象的形状、纹理和光谱特征,并优选最佳特征子集;④最后基于支持向量机(SVM)完成面向对象的影像分类。通过对福州IKONOS影像数据实验,结果表明融入影像特征后的分割效果明显优于原始影像的分割结果,而信息最大化(mRMR)的特征选择能够快速地获得较好的特征子集。通过与eCognition最邻近分类方法比较,表明本文方法的分类总体精度大约提高了6%,效果显著。     

A method for obtaining and applying classification parameters in object-based urban rooftop extraction from VHR multispectral images

Object-based methods of urban feature extraction from high spatial resolution remotely sensed data rely on semantic inference of spatial and contextual classification parameters in scenes of regular spatial or material composition. In this study, a supervised statistics-based method of determining and applying discretive parameters of rooftops in urban scenes of irregular composition is presented. After preprocessing to pansharpen IKONOS image data, the method includes the following steps: (1) image segmentation; (2) supervised object-based classification into broad spectral classes including impervious surfaces; (3) spectral, spatial, textural and contextual parameters are developed from statistical comparison of the sample rooftop and other impervious surface objects and (4) these parameters are implemented in a fuzzy logic rule base to separate rooftops from other impervious surfaces. Classification of a test scene results in 93% accuracy of rooftop identification, demonstrating the applicability of the method to the discrimination of spectrally similar but semantically variable classes.     

The influence of urban structures on impervious surface maps from airborne hyperspectral data

Airborne hyperspectral data fulfills the high spectral and spatial resolution requirements of urban remote sensing applications. Its high spectral information content enables delineating impervious areas including the separation of built-up and non built-up surfaces, thus being of high relevance for many urban environmental applications. However, two phenomena related to surface structure negatively impact the accuracy of maps from such airborne data sets: (1) displaced buildings that lead to confusion between the class built-up and adjacent non built-up areas as a function of building height and view-angle; (2) urban street trees obscuring impervious surface underneath. Both effects have so far not been investigated from airborne hyperspectral data and potential sources of inaccuracy are usually not differentiated in analysis utilizing such data. Thus, the positive influence of hyperspectral information might have been undervalued in many cases. We set up an analysis scheme that allows for separately quantifying sources of error when producing land cover maps from urban areas. Given reliable cadastral information on building extent and street network, a detailed analysis for a relatively large Hyperspectral Mapper data set acquired over Berlin, Germany, was performed. Results show that both building displacement and impervious surface obscured by tree crowns are of great impact: at large view-angles, building displacement adds up to 16% error compared to nadir regions; more than 30% of the street area is classified as vegetation. Moreover, both effects show irregularities that prohibit empirical correction: misclassification due to building displacement also depends on view-direction, i.e. illumination properties and shadow, while the influence of trees differs significantly along streets and inside residential areas. Results from this work underline the necessity to consider all image processing steps when evaluating the accuracy and reliability of remote sensing products and they depict directions for future methodological development.     

Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and watershed resource management, require accurate and up‐to‐date geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical and InSAR data in urban impervious surface mapping at the sub‐pixel level was investigated. A case study in Hong Kong was conducted for this purpose by applying a classification and regression tree (CART) algorithm to SPOT 5 multispectral imagery and ERS‐2 SAR data. Validated by reference data derived from high‐resolution colour‐infrared (CIR) aerial photographs, our results show that the addition of InSAR feature information can improve the estimation of impervious surface percentage (ISP) in comparison with using SPOT imagery alone. The improvement is especially notable in separating urban impervious surface from the vacant land/bare ground, which has been a difficult task in ISP modelling with optical remote sensing data. In addition, the results demonstrate the potential to map urban impervious surface by using InSAR data alone. This allows frequent monitoring of world's cities located in cloud‐prone and rainy areas.     

Quantifying high‐resolution impervious surfaces using spectral mixture analysis

Impervious surface distribution and its temporal changes are considered key urbanization indicators and are utilized for analysing urban growth and influences of urbanization on natural environments. Recently, urban impervious surface information was extracted from medium/coarse resolution remote sensing imagery (e.g. Landsat ETM+ and AVHRR) through spectral analytical methods (e.g. spectral mixture analysis (SMA), regression tree, etc.). Few studies, however, have attempted to generate impervious surface information from high resolution remotely sensed imagery (e.g. IKONOS and Quickbird). High resolution images provide detailed information about urban features and are, therefore, more valuable for urban analysis. The improved spatial resolution, however, also brings new challenges when existing spectral analytical methods are applied. In particular, a higher spatial resolution leads to reduced boundary effects and increased within‐class variability. Taking Grafton, Wisconsin, USA as a study site, this paper analyses the spectral characteristics of IKONOS imagery and explores the applicability of SMA for impervious surface estimation. Results suggest that with improved spatial resolution, IKONOS imagery contains 40–50% of mixed urban pixels for the study area, and the within‐class variability is a severe problem for spectral analysis. To address this problem, this paper proposes two approaches, interior end‐member set selection and spectral normalization, for SMA. Analysis of results indicates that these approaches can reasonably reduce the problems associated with boundary effects and within‐class variability, therefore generating better impervious surface estimates.     

Building Height Estimation from GF-2 Image based on Morphological Shadow Index

GF-2 is a high resolution earth observing satellite with sub\|meter resolution which is developed by our own technique.To estimate urban building height based on GF\|2 remote sensing image combined with the idea of mathematical morphology and object\|oriented classification.First of all,segment image based on multi\|scale segmentation.Then extract shadow and calculate its length based on object\|oriented classification combined with spectral,shape,Morphological Shadow Index (MSI) and other features.In the end,estimate building height based on the geometrical model of satellite,sun and building and then accuracy evaluation and error analysis are carried out by using the field measurement data.Experimental results showed that 90% of the buildings’ absolute error is less than 1 m.This experiment demonstrate that the method can extract the height of urban building from the GF\|2 image effectively and the immense potential of domestic high resolution remote sensing image in applications on urban building information extraction.     

An integrated soft and hard classification approach for evaluating urban expansion from multisource remote sensing data: a case study of the Beijing–Tianjin–Tangshan metropolitan region,China

Integrating soft and hard classification to monitor urban expansion can effectively provide comprehensive urban growth information to urban planners. In this study, both the impervious surface coverage (as a soft classification result) and land cover (as a hard classification result) in the Beijing–Tianjin–Tangshan metropolitan region (BTTMR), China, were extracted from multisource remote sensing data from 1990 to 2015. Then, we evaluated urban expansion based on centre migration, standard deviation ellipse, and spatial autocorrelation metrics. Furthermore, the differences between the soft and hard classification results were analysed at the landscape scale. The results showed that (1) the impervious surface area increased considerably over the past 25 years. Notably, the areas of urban built-up land and industrial production land increased rapidly, while those of ecological land and agricultural production land seriously decreased. (2) The distribution of impervious surfaces was closely related to the regional economic development plan of ‘One Axis, Two Wing, and Multi-Node’ in the BTTMR. (3) The contributions of different land use types to impervious surface growth ranked from high to low as follows: urban built-up land, rural residential land, industrial production land, agricultural production land, and ecological land. (4) The landscape metrics varied considerably based on the hard and soft classification results and were sensitive to different factors.