针对资源三号卫星影像水体提取的谱间关系法

针对现阶段水体提取的数据源多是国外测绘卫星数据且需要多波段、丰富光谱信息的问题,通过研究资源三号卫星影像中水体的光谱特征以及阴影的光谱特征,提出一种在资源三号卫星影像中水体提取的方法。运用单波段阈值法、归一化差分水体指数法、支持向量机法、基于阈值的谱间关系法4种方法对提取水体的效果进行比较分析。实验结果表明,本文提出的基于阈值的谱间关系法提取水体的效果比另外3种方法有明显提升,能有效剔除阴影的影响,并且能较好地提取影像中的细小水体。     

巢湖流域水体信息提取方法研究

以Landsat-5 TM影像为数据源,在对研究区典型地物光谱特征值分析的基础上,利用各水体指数模型之间的关系,探讨了二值化提取水体中阈值确定的其他方法;通过对地物光谱值的分析,发现水体由于泥沙等含量的不同以及深浅的不同,其灰度值变化范围较大,与阴影的重叠较多,而采用谱间关系法,不能确定合适的阈值。提出了加权各波段灰度值并设定阈值去除阴影的方法--加权灰度阈值法,可以有效地去除阴影。研究表明结合水体指数模型法和加权灰度阈值法可以准确地提取水体信息。     

ETM+影像水体提取方法研究

以Landsat-7的ETM+为数据源,通过实验比较了阈值法、谱间关系法、改进的归一化差异水体指数法以及分类提取法对水体进行提取的效果,得出了谱间关系法易消除阴影的误提,单波段阚值法易消除居民地误提的结论,提出了综合谱间关系法和阎值法的方法,取得了好的提取效果,有效减少了误提现象.同时,针时阈值选取需经过反复试验的问题,提出了对(TM2+TM3)-(TM4+TM5)和TM5叠加影像进行聚类分割自动提取出水体,提取精度高且避免了阈值选取的繁琐.     

基于分布式计算的遥感图像水体识别研究

为了提高遥感数据的处理速度,解决遥感信息提取中的数据密集与计算密集问题,将并行计算的思想引入到遥感图像的处理与信息提取中,构建基于Landsat ETM+影像的分布式遥感图像水体提取模型。以渭干河流域为研究区,利用单波段阈值法、多波段谱间关系法、水体指数法等方法进行水体信息自动提取的实验。实验结果表明,该模型具有较高的识别精度,能够快速识别水体,并具有稳定的可扩展性和伸缩性。     

基于Landsat TM影像的圩田结构信息提取与分析--以皖东南圩田区为例

皖东南圩田是中国主要圩田区之一，圩田结构特征主要反映在圩田水网以及圩田堤坝的组合上，对狭窄沟渠及水体与堤坝形成的混合像元的结构信息提取是圩田结构信息提取成功的关键。基于陆地卫星Landsat TM数据，采用了谱间关系法与阈值法两种方法进行圩田结构信息提取实验，实验表明：谱间关系法可以精确的提取水体信息，由于水体的混合像元不易与其它地物区分，所以无法合理的处理混合像元；阈值法中发现在第5波段，水体阈值的一定离散范围内的混合像元主要表现为圩田水网与圩田堤坝的组合，但易多提取居民地信息。通过对闲值法提取信息的二值图像与居民地信息二值图像进行布尔运算来剔除冗余信息，成功提取了圩田结构信息，探讨了圩田结构的4种类型并分析了它们的功能与形成背景。     

基于TM影像的典型内陆淡水湿地水体提取研究

水是维系湿地生态系统稳定和健康的决定性因子,利用卫星遥感影像快速、准确地提取湿地水体信息已经成为湿地调查、研究与保护的重要手段。鉴于TM遥感影像具有较高的空间分辨率、波谱分辨率、极为丰富的信息量、较高的定位精度和相对较低的价格,其必然成为近一段时期湿地调查、研究与保护的重要数据源之一。研究基于TM遥感影像,运用多种方法针对典型内陆淡水湿地的水体信息进行了提取实验,通过对实验结果的分析得出：在面积的准确性、提取的准确度以及视觉效果3种指标下,光谱分类法较其它方法效果要好,其次为单波段阈值分析法与植被指数法,较差的是多波段谱间关系法与水体指数法;影响提取效果的主要原因是湿地水体提取不够完全,这是由影像的分辨率及湿地特殊的水文条件所造成的,采用像元分解及多源遥感数据融合技术将成为提高水体提取精度的重要手段。     

Landsat数据水体自动提取研究进展

水体自动提取是当前遥感技术应用研究的热点之一。在简述水体遥感识别机理的基础上,回顾了国内外常用的Landsat数据水体自动提取方法,并将其划分为单波段阈值法、多波段谱间关系法、水体指数法和分类后提取法4类进行综合阐述,最后从辐射校正、混合像元和图像二值细化3方面对水体自动提取的未来研究方向做了展望,以期为水体自动提取及相关研究提供参考。     

基于TM影像的几种常用水体提取方法的比较和分析

随着遥感技术的飞速发展,利用遥感数据来进行水资源的监测、调查和分析已成为一种必然的趋势。从遥感影像中快速、准确地提取水体信息,是进行水资源调查和监测的一种重要的方法和手段。目前进行水体提取的方法有很多,本文选取了常用的3种水体提取方法,即单波段阈值法、基于阈值的多波段谱间关系法、基于阈值的水体指数法,然后分别选取典型的武汉平原地区和宜昌山地地区为研究区,以Landsat5TM影像为数据源,通过实验来比较和分析这3种水体提取方法分别在平原地区和山地地区的优势和不足。     

湖泊水体遥感提取方法比较研究

本文以位于我国不同地区的10个湖泊为研究区域,对波段差值模型、FAI模型以及NDWI和近红外波段模型等3种水体提取模型进行了比较,分析其在MODIS数据湖泊水体识别方面的效果。模型中的阈值分别采用大津法和迭代法计算。结果表明,波段差值模型在西部地区湖泊的应用效果较好,而NDWI和近红外波段模型在东部地区湖泊的应用效果较好,两者总体上比FAI模型更为准确地提取了湖泊水体信息。此外,大多数情况下大津法和迭代法得到的阈值是相近的。     

基于GF-2遥感影像的一种快速水体信息提取方法

在高分辨率遥感影像中,水体与阴影(尤其是高大建筑物阴影)、暗色地物不易区 分。针对 GF-2 遥感影像的光谱特性的大量实验研究,提出了一种新综合水体指数法(NCWI)来 增强水体区域信息;同时利用改进的 OSTU 结合鸡群算法(CSO)快速自适应地确定最佳分割阈 值,进而得到最终的水体区域。将其同归一化 NDWI、改进谱间关系法、主成分分析综合法等 常见水体信息提取方法应用于 GF-2 遥感影像水体信息提取,利用采用实地采样和人工解译的 混淆矩阵对提取的水体区域结果进行精度验证和对比分析,从而验证了其有效性和高效性。4 个实验区域的结果证明,该算法可以快速有效地提取水体信息,精确度分别达到 97.82%, 97.44%,92.13%,96.94%。     

基于多种变换的遥感图像新型融合方法

针对多光谱图像空间分辨率低这一特点,提出一种在PCA变换基础上,利用小波变换和高通滤波相结合的图像融合算法。实现了ETM+全色波段与ETM+多光谱波段图像的融合,并从空间纹理信息,光谱真实性两个方面进行定性和定量评价。研究表明,该融合算法产生的光谱失真较小,同时很大程度地保持了高分辨率全色波段的空间纹理细节信息,是一种较好的图像融合方法。     

基于知识决策树的城市水体提取方法研究

针对城市水体与建筑物阴影、沥青路面和浓密植被等暗地物的光谱混淆性,构建了结合光谱特征和空间特征的城市水体提取知识决策树。其基本思路为：首先 利用短波红外波段提取暗地物,其次分别利用浓密植被在近红外波段和沥青路面在红波段中的反射率剔除这两类暗地物,再次利用空间密度特征剔除建筑物阴影,最 后根据面积对水体进行补充识别。与现有方法相比,本方法提出了城市水体提取中需关注的暗地物类型并开展针对性特征分析,并利用由噪声环境下密度聚类方法 （DBSCAN）描述的空间密度特征区分城市水体和建筑物阴影。对北京城区SPOT 5多光谱影像开展的实验得到的检测率为86.18%,虚警率为13.82%,表明本方法是基于 中分辨率多光谱影像提取城市水体的有效方法。     

An automatic shadow detection method for high-resolution remote sensing imagery based on polynomial fitting

Most existing shadow detection models and algorithms require extensive calculations and have difficulties effectively removing features, such as water bodies, some dark objects and bluish ground objects. In this paper, we propose a high-resolution automatic shadow extraction algorithm based on the process of histogram fitting. First, the histogram of the whole image is fitted by fourth and fifth-degree polynomials according to the histogram difference of the near-infrared bands of different shadow areas in the remotely sensed image. Second, the shadow area is preliminarily extracted based on the relationships between the shadow features of the remote sensing image and the intersections of the fourth- and fifth-degree polynomials. Then, the normalized difference water index (NDWI) is applied to extract the water bodies. Finally, to obtain the shaded area, the scanning line seed filling algorithm is applied to remove the water bodies falsely detected as shadows in the preliminary shading extraction. The proposed algorithm is evaluated by using the various high-resolution images including GaoFen-1 (GF-1), GaoFen-2 (GF-2), QuickBird2, and ZiYuan-3 (ZY-3), as well as an elaborate comparison to histogram threshold segmentation algorithms such as Component 3 (C3) algorithm, multi-elements extraction algorithm multi-band detection algorithm, and spectral correlation algorithm based on spectral features. The results of experiment showed that the proposed algorithm could extract the shadows of various images, achieve satisfied results, and completely remove water bodies.     

基于暗通道原理从单幅遥感影像估计山脉高程的方法

根据单幅遥感图像估计高程信息可以应用于滑坡、泥石流等自然灾害的检测,因此,提出了一种基于暗通道原理的单幅遥感图像高程值提取算法,并对山脉的阴影部分进行了改进。运用了暗通道原理,同时给出了一种克服山脉阴影影响的解决策略。对大量山脉遥感图像进行了定量和定性分析,提出的算法能够得到较准确的山脉相对高程信息。综上所述,改进算法能够有效提取出包含阴影的单幅山脉遥感图像的高程信息。     

基于压缩数据维的城市建筑用地遥感信息提取

通过压缩数据维的方式，研究城市建筑用地信息准确提取的原理和方法。通过对城市土地利用类型的分析，选取了归一化差异建筑指数、修正归一化差异水体指数和土壤调节植被指数来代表城市建成区的3种最主要地类——建筑用地、水体和植被。通过将ETM 影像原有的7个波段压缩为由它们衍生的这3个采用比值运算构成的指数波段，大大压缩了数据维数、减少了数据的相关度并降低了不同地类的光谱混淆性。因此采用简单的最大似然分类和掩膜处理技术，就可以将城市建筑用地信息提取出来，其精度可达91．2％。     

Improving forest type classification using the vegetation local difference index

A spatial feature extraction method was applied to increase the accuracy of land-cover classification of forest type information extraction. Traditional spatial feature extraction applications use high-resolution images. However, improving the classification accuracy is difficult when using medium-resolution images, such as a 30 m resolution Enhanced Thematic Mapper Plus (ETM+) image. In this study, we demonstrated a novel method that used the vegetation local difference index (VLDI) derived from the normalized difference vegetation index (NDVI), which were calculated based on the topographically corrected ETM+ image, to delineate spatial features. A simple maximum likelihood classifier and two different ways to use spatial information were introduced in this study as the frameworks to incorporate both spectral and spatial information for analysis. The results of the experiments, where Landsat ETM+ and digital elevation model (DEM) images, together with ground truth data acquired in the study area were used, show that combining the spatial information extracted from medium-resolution images and spectral information improved both classification accuracy and visual qualities. Moreover, the use of spatial information extracted through the proposed method greatly improved the classification performance of particular forest types, such as sparse woodlands.     

基于矿物填图和构造解译的夹皮沟金矿找矿方法研究

针对夹皮沟找矿,在两个方面研究:①采取掩膜处理、阈值切割、比值、光谱角等多种方法综合分析并去除目标影像的植被、阴影、河流水体及其冲积物等众多干扰信息;②采用单窗算法对ETM遥感影像中的热红外波段进行地表温度反演,根据地表温度的反演影像进行遥感解译构造,对蚀变异常区域和构造解译影像做叠合分析,找出成矿远景区和成矿有利区,圈定可能的成矿靶区,得到了很好的结果。     

Landsat 8 Data Mountain Shadow Removal based onBrightness Recovering Model

Mountain region in remotely sensed imagery are usually covered by shadows，which reduce the accuracy of information extraction.Therefore，in this paper a method based on intensity restoration is putting forward necessarily.First，Shadow Detection (SD) was constructed by the Max function and the band ratio to identify shadows.Thus，mountain shadows were extracted combined with the slope factor and SD，through the grid randomly arranged verification point verification accuracy.Second，the intensity curve model of the shadow area was fitted by ground data of the shadow and the transition rules of pixel intensity from the shadow to non\|shaded area.Third，the intensity restoration model was established by the derivative function of intensity curve to remove shadows.The results of the model on Changting Landsat 8 imagery indicated the extraction accuracy of the mountain shadow was 99.06% and the Kappa coefficient was 98%；According to the cluster analysis，the restoration and non\|shaded samples were the same type；Processed by the intensity restoration model，the average intensity of the shadow was increased by 13%，and the standard deviation was reduced by 80% and the clustering distances was reduced by 96%.respectively，average intensity of the shadow increased by 6.7%，the standard deviation was reduced by 73.7% and the clustering distances was reduced by 88.3% when compared with ATCOR_3，and average intensity of the shadow reduced by 1.8%，the standard deviation was increased by 6.7% and the clustering distances was reduced by 90% when compared with unitary linear restoration model.In the process of removing the mountain shadows，the intensity restoration method is neither replacing the shaded pixels nor interference with non\|shaded pixels and could preserve the spectral and intensity characteristics of shaded pixels better.  ；     

公路车流量视频检测方法

针对视频车流量检测容易受背景以及车辆阴影等因素影响的问题,提出了一种自适应背景差分结合阴影去除的车流量检测方法。首先,建立自适应背景提取模型;然后,利用差分法从视频检测区域提取包含阴影的车辆目标,并进行二值化处理和孔洞填充;接着依据阴影区域相对于车辆区域灰度较小的特点,从填充后的二值图像阴影区域向车辆区域方向进行像素值比较,从而检测并去除阴影;最后,通过设定两排检测窗口进行车流量计数。实验结果表明,该方法受背景和车辆阴影等影响较小,在不同气候环境下具有较高的车流量检测准确率。     

Digital terrain analysis using Landsat‐7 ETM+ imagery and SRTM DEM: a case study of Nevsehir province (Cappadocia), Turkey

A three‐dimensional (3D) model of land‐use/land‐cover (LULC) and a digital terrain model of Nevsehir province (Cappadocia), Turkey, were generated and analysed using a Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) multispectral image set and a Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM). Stream drainage patterns, lineaments and structural‐geological features (landforms) were extracted and analysed. In the process of analysing and interpreting the multispectral images of geological features, criteria such as colour and colour tones, topography and stream drainage patterns were used to acquire information about the geological structures of the land, including as geomorphological, topographic and tectonic structures. Landsat‐7 ETM+ multispectral imagery and an SRTM DEM of the study region were used experimentally for classification and analysis of a digital terrain model. Using the multispectral image data, the LULC types were classified as: settlement (1.2%); agricultural land (70.1%); forest (scrubland, orchard and grassland) (2.9%); bare ground (25.5%); and water bodies (lakes and rivers) (0.3%) of the study area (5434 km²). The results of the DEM classification in the study area were: river flood plain (11.3%); plateau (52.3%); high plateau (28.4%); mountain (7.6%); and high mountain (0.3%). Lineament analysis revealed that the central Kizilirmak River divides the region into two nearly equal parts: the Kirsehir Plateau in the north and the Nevsehir Plateau in the south. In terms of the danger of catastrophe, the settlements of Kozakli, Hacibektas and Acigol were found to be at less risk of earthquake and/or flooding than those of Avanos, Gulsehir, Urgup, Nevsehir, Gumuskent and Derinkuyu, which are located on river flood plains and/or the main stream drainage channels, particularly stream beds, where the lineaments are deep valleys or fracture or fault‐line indicators.