面向对象滑坡信息提取中DEM空间分辨率影响分析

数字高程模型(DEM)是识别滑坡的重要特征,而在实际应用中通常难以获取研究区的高分辨率DEM数据。为分析DEM分辨率对滑坡提取的影响并确定满足应用的分辨率要求,将原始10 m分辨率DEM重采样为空间分辨率不同的15、20、30、50、75与100 m,采用DEM及其衍生数据辅助高分遥感影像的面向对象滑坡信息提取方法进行实验。实验结果表明:当DEM分辨率小于30 m时,对面积大于5 000 m²的滑坡能得到较好的识别和分类;大于30 m时虽难以区分出滑坡类型,但通过调整部分规则参数仍能实现对滑坡的监测。该研究对于准确提取滑坡信息所需DEM产品的选择具有一定的指导意义和参考价值。     

以SPOT5遥感数据为例比较基于像素与面向对象的分类方法

以SPOT5高空间分辨率遥感影像为研究数据,采用面向对象的遥感影像分类方法,将基于像素的分类精度与基于影像对象的分类精度进行对比分析。结果表明:基于像素的分类方法在处理高空间分辨率遥感影像时具有一定的局限性,而面向对象的分类方法充分利用了影像中的光谱信息、几何结构、空间信息及上下文信息,在高空间分辨率遥感影像分类方面更具有优势,有良好的应用前景。     

空间特征在遥感影像分析中的应用

本文基于神经系统科学有关“特征”的研究成果,对“空间特征”的定义及其在高空间分辨率遥感影像目标识别与分类中的应用进行了研究。研究表明,空间特征包括空间原始特征、空间功能特征两种类型,其中,空间原始特征是对空间实体组成要素特性的反映,是有关空间实体的识别特征(说明“是什么”),而空间功能特征则是空间实体的功能性分类标志,是有关空间实体的分类特征(说明“干什么”)。文章还具体结合一个基于高空间分辨率遥感影像对机场目标进行识别与分类的实验,对有关结论做进一步的阐释。     

IKONOS影像白泡云识别方法研究

高空间分辨率IKONOS影像应用在海洋遥感时,白泡云的强反射特性严重影响了水体信息的提取。本文利用归一化处理后的IKONOS影像数据研究分析了白泡云与其背景地物的光谱特征。通过光谱一阶微分运算形成特定的数学参数使得地物的光谱特征参量化,提取白泡云光谱特征,研究开发了白泡云遥感识别模型。试验结果表明该识别方法准确度高,在识别水体中白泡云区域的基础上可有效处理海洋遥感水体信息提取过程中白泡云的干扰影响。     

基于AVHRR和TM数据的时间序列较高分辨率NDVI数据集重构方法

由于技术条件的限制,一个传感器很难同时具有高空间分辨率和高时间分辨率。然而,在高分辨率尺度上监测地表景观季节性变化的能力是全球的迫切需要,融合周期短、覆盖范围广与分辨率高、周期长的遥感数据是一种较好的方法。基于AVHRR时间分辨率高和TM空间分辨率高及其数据积累时间长的特点,选择若尔盖高原为研究区域,在改进ESTARFM方法的基础上,对TM NDVI和AVHRR NDVI进行融合,构建高时空分辨率的NDVI数据集。研究结果表明:该方法能有机结合AVHRR NDVI的时间变化信息与TM NDVI的空间差异信息,有效实现高时空分辨率NDVI数据集的重构,3景预测高分辨率NDVI与MODIS NDVI产品相关系数分别达到了0.89、0.91和0.85。该方法能够在时间上保留高时间分辨率数据的时间变化信息,同时在空间上反映高空间分辨率数据的空间差异信息,从而为有效构建相对高分辨率时间序列NDVI数据集提供了可能的方法。     

遥感技术在我国滑坡研究中的应用综述

中国滑坡遥感研究与应用已有30多年的发展历史,作为滑坡调查的主要手段在大型工程建设中的滑坡灾害调查及危险性评价中发挥了重要作用。从4方面阐述了遥感技术在我国滑坡研究中的应用：① 区域滑坡灾害遥感调查;② 大型单体滑坡遥感调查;③ 滑坡灾害遥感监测;④ 遥感应用于滑坡风险评估。随着遥感技术理论的逐步完善和遥感图像空间分辨率、时间分辨率与波谱分辨率的不断提高,遥感技术已成为滑坡灾害调查、动态监测与预警、灾情实时调查与损失评估等工作中不可缺少的重要手段之一。     

基于光谱、空间和形态特征的面向对象滑坡识别

地震经常诱发数量众多,覆盖范围广的滑坡并造成极大危害,因此,需要对大范围的滑坡灾害进行快速评估。随着遥感影像分辨率的提高,面向对象分类方法在这方面的应用比传统的目视解译和基于像素的方法更具优势。但是,目前基于面向对象方法的滑坡识别研究还相对较少,而且通常针对小范围的研究区。基于SPOT5 2.5m多光谱影像,提出一种综合光谱、空间、地形和形态特征的面向对象滑坡自动识别方法,并应用于较大范围研究区。结果表明:面向对象滑坡自动识别方法能将研究区内95%的滑坡识别出来,综合考虑滑坡的过度提取与遗漏提取情况,滑坡提取质量为74.04%,效果较好,能够快速、有效地识别大范围的滑坡。该方法可以应用于对地震或强降雨引起的大范围滑坡灾害进行快速评估,为灾后应急救援和恢复重建工作提供参考。     

多源遥感影像融合在太湖蓝藻监测中的应用

基于空间分辨率分别为1 100 m和500 m的NOAA/AVHRR和EOS/MODIS遥感数据,考虑遥感影像区域内各像素之间的区域特征,设计了基于小波分析的区域能量融合方法(REFS_wt),低频小波系数采用平均值而高频系数采用区域能量法,并与基于像素灰度值的区域能量法(REFS_pl)进行融合性能比较,结果表明REFS_wt法的融合性能明显优于REFS_pl。将此方法应用于太湖蓝藻监测,将空间分辨率较低的AVHRR影像蓝藻水华信息与较高分辨率的MODIS影像融合,得到较高分辨率的太湖蓝藻水华遥感监测图,融合图像信息量和清晰度都有所提高。     

资源三号卫星在汶川震区滑坡快速识别中的应用方法研究

针对当前采用野外人工调查和遥感目视解译获取地震灾区滑坡信息存在主观性强、耗时费力等缺点。以汶川震区为实验区,应用资源三号(ZY-3)卫星影像,采用多尺度最优分割方法构建多层次识别对象,融合光谱、纹理、几何等影像特征和地形特征建立多维滑坡识别规则集合,以及基于高分辨率影像认知模式与场景理解过程提出滑坡分层识别模型,从而实现滑坡空间分布及其滑源区、滑移区和堆积区的快速识别。通过实验结果发现最低识别精度为82.97%,而滑坡的堆积区最容易识别。研究结果验证了该方法及资源三号卫星数据快速识别地震滑坡信息的有效性,可为震区滑坡调查提供技术支持,并可促进国产卫星的推广应用。     

基于均值漂移聚类的油罐识别

从高空间分辨率遥感影像中自动的提取油罐目标和具有重要的意义。针对传统方法计算复杂度高的不足,结合油罐和油库区域在遥感影像中体现的光谱和空间分布特点,提出一种基于均值漂移迭代聚类的油罐自动识别算法。算法基于均值漂移的分割算法进行区域的多层次合并,同时在每个分割尺度上依据形状参数检测油罐。多幅遥感影像的实验结果表明了算法具有较高的识别精度和的较低时间复杂度。     

Research on Wenchuan Earthquake-induced LandslidesRapid Recognition from ZY-3 Imagery

Presently,regional earthquake-induced landslides is mainly obtained by field survey and visual interpretation from remote sensing images; but these methods are objective,and time-consuming.In this study,with a main data source of domestic high-resolution remote sensing images from ZY-3 satellite as well as the study area of the Wenchuan earthquake region,objects of multilevel landslides were established using the multi-scale optimum partition method based on in-depth analysis of landslide features.A recognition rule set of multi-dimensional landslides was also built through the combination of topographic features and image features,such as spectrum,texture,and geometry.Additionally,recognition models for landslide stratification were proposed based on the recognition models of high-resolution images and an understanding of the scenes.Through all of the aforementioned efforts,the spatial distribution of the seismic landslide as well as the sliding source area,transport area,and depositional area can be identified intelligently.The analysis results of the experimental area showed a minimum recognition accuracy of 82.97%,with the depositional zone of landslides being the easiest zone to recognize,and the effectiveness of the proposed method as well as ZY-3 data.These findings may provide technical support for regional earthquake-induced landslides investigations and further promote geological hazard application of domestic high-resolution satellites.      

基于NDVI变化检测的滑坡遥感精细识别

随着遥感技术的发展,高分辨率的卫星影像数据逐渐丰富,滑坡灾害的信息提取被进一步推进,当前滑坡灾害应急调查主要以目视解译和野外调查为主,费时费力,难以满足灾后救援的迫切需求。面向像元和面向对象的单时相滑坡遥感信息提取方法等存在着滑坡过识别、误识别的问题。因此,在此提出以滑坡前后多时相遥感影像为数据源的变化检测滑坡识别方法,首先根据归一化植被指数（NDVI）进行基于像元的变化检测确定滑坡预选区,再结合面向对象的几何规则完成滑坡的精细识别,这种基于变化检测和几何规则相结合的方法能有效排除道路、建筑、裸地等光谱特征与滑坡相似的非滑坡部分。以九寨沟滑坡为例,采用高分一号8 m分辨率多光谱相机2015年8月1日的影像（滑前）以及2017年8月16日的影像（滑后）作为数据源,进行滑坡识别实验。结果表明,和面向对象的单时相方法相比,基于变化检测和几何规则相结合的多时相方法滑坡提取的精度较高,制图精度高达88.80%,用户精度高达81.19%,都大幅超过面向对象单时相法的精度,漏分误差及错分误差分别下降23.22%和11.72%,可为有效组织滑坡灾后救援与重建工作提供可靠依据。     

Object-oriented Detection of Landslides based on the Spectral，Spatial and Morphometric Properties of Landslides

Earthquakes in mountain area often induce hundreds of thousands of landslides resulting in destructive casualties and economic damage.It is urgent needed to rapidly detect the extent areas of the landslides.With the advent of very high resolution satellite remote sensing,the application of the object\|oriented classification method in this area have significant advantage comparing to those of visual interpretation and pixel\|based methods.However,the study of object\|oriented landslide detection is relatively few,and the study usually has a small study area.The method of object\|oriented rapid identification of landslides based on the spectral,spatial and morphometric properties of landslides and a 2.5m SPOT5 multi\|spectral image is proposed in this paper and is applied in a relatively large study area.The normalized difference vegetation index (NDVI) threshold was set to remove vegetation objects and obtain landslide candidates.Then,the spectral characteristics,texture,terrain features and context of the image were used to build indicators to gradually separate the landslide from false positives.The small scale chessboard segmentation was conducted to further eliminate vegetation objects and get the landslide objects.The object\|oriented detection results show that the adopted method can recognize about 95% of the landslides in the study area.When considering the landslide excessive detection and omissions,the landslide detection quality percentage of the proposed method is 74.04%.Hence,the method proposed in the article can help to rapid assess landslide disasters caused by earthquakes or heavy rainfalls,providing a reference for post\|disaster emergency relief and reconstruction work.     

Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun,Korea

The aim of this study is to evaluate the hazard of landslides at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. The factors that influence landslide occurrence, such as slope, aspect and curvature of the topography, were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, age, diameter and density of timber were extracted from the forest database. The lithology was extracted from the geological database and lineaments were detected from Indian Remote Sensing (IRS) satellite images. The land cover was classified based on the Landsat Thematic Mapper (TM) satellite image. Landslide hazard areas were analysed and mapped, using the landslide-occurrence factors, by the probability–likelihood ratio method. The results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the hazard map and the existing data on landslide locations.     

滑坡稳定性与土地利用关联规则挖掘及应用 ——以三峡库区秭归到巴东段为例

三峡库区滑坡灾害广泛发育,其稳定性受土地利用变化等人类工程活动的影响。采用数据挖掘技术研究库区土地利用变化对滑坡稳定性的影响及其规律,利用三个时相的遥感影像得到实验区滑坡面上两个时段间的土地利用变化监测图,用Apriori算法挖掘出滑坡稳定性与土地利用变化类型之间的强规则,用马尔可夫链模型预测滑坡面上土地利用的变化趋势,将预测结果用于对滑坡稳定性发展的分析评估。通过实验分析,所采用的方法可用于预测滑坡稳定性变化趋势,为滑坡灾害的监测预警提供决策支持。     

Earthquake-induced landslide dam in the Kashmir Himalayas

The Kashmir earthquake of 2005 triggered numerous landslides in inaccessible areas of the western Himalayas, which could be mapped using satellite remote sensing. The largest recorded seismicity-induced landslide dammed a river, which resulted in the formation of a stream in the toe region and created two reservoirs that pose an enormous threat in the event of a landslide dam breach. Using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data sets corresponding to the pre- and post-earthquake period and derived digital elevation models, landslide-induced lakes were monitored. The aerial extent, depth profile and volume of both the reservoirs were determined. This study has demonstrated the utility of ASTER data in providing valuable information that is critical for hazard mitigation in case of a landslide dam breach.     

Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographical Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Thematic Mapper (TM) satellite images; and the vegetation index value from Système Probatoire de l'Observation de la Terre (SPOT) satellite images. Landslide hazardous areas were analysed and mapped using the landslide‐occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better in prediction than probabilistic model.     

一种高空间分辨率遥感影像信噪比测定方法

信噪比是用来评价遥感影像质量一个重要的辐射特性参数。通过合适的方法精确地测定出遥感影像对应的信噪比参数,可以帮助数据提供者或用户更好地预测和提高遥感影像的信息提取能力。现有的信噪比测定方法大多基于中低空间分辨率遥感影像进行,并不能很好地适用于高空间分辨率遥感影像。针对现有信噪比测定方法的这一局限性,从高空间分辨率遥感影像本身特点出发,通过自适应地划分DN值子区间,以一定百分比最小局部标准差的平均值估算每一DN值子区间对应的噪声大小,进而估算每一DN值子区间对应的信噪比。该方法充分考虑了高分遥感影像内空间细节信息、边缘信息以及纹理信息较强而使得均值区域很难获取的特点。研究表明:改进的方法对于估算高分遥感影像信噪比具有较好的适用性。