Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery

This article first examines three existing methods of delineating open water features, i.e. the normalized difference water index (NDWI), the modified normalized difference water index (MNDWI) and a method combining the near-infrared (NIR) band and the maximum likelihood classification. We then propose two new methods for the fast extraction of water features in remotely sensed imagery. Our first method is a pixel-based procedure that utilizes indices and band values. Based on their characteristic spectral reflectance curves, waterbodies are grouped into three types – clear, green and turbid. We found that the MNDWI is best suited for identifying clear water. Green water has its maximum reflectance in Landsat Thematic Mapper (TM) band 4 (NIR band), whereas turbid water has its maximum reflectance in TM band 5 (mid-infrared band). Our second method integrates our pixel-based classification with object-based image segmentation. Two Landsat scenes in Shaanxi Province, China, were used as the primary data source. Digital elevation models (DEMs) and their derived slope maps were used as ancillary information. To evaluate the performance of the proposed methods, extraction results of the three existing methods and our two new methods were compared and assessed. A manual interpretation was made and used as reference data. Results suggest that our methods, which consider the diversity of waterbodies, achieved better accuracy. Our pixel-based method achieved a producer's accuracy of 92%, user's accuracy of 90% and kappa statistics of 0.91. Our integrated method produced a higher producer's accuracy (95%), but a lower user's accuracy (72%) and kappa statistics (0.72), compared with the pixel-based method. The advantages and limitations of the proposed methods are discussed.     

基于知识和遥感图像的神经网络水质反演模型

为进一步提高遥感图像水质反演的精度，提出了一种基于知识和遥感图像相结合的神经网络水质反演模型。该模型利用遥感图像数据以及与水质相关的知识数据作为BP神经网络的输入，经训练后，确定神经网络的结构，在训练好的BP神经网络基础之上对水质进行反演。以中国太湖为例进行实证研究，实验中，使用的知识数据包括太湖的地理信息知识和先对太湖TM图像上的水域解译进而对水质进行分类的知识。实验结果表明，本文提出的水质反演模型较常规的线性回归模型和传统的神经网络模型有更高的反演精度。     

基于Landsat8的地表温度反演算法研究——以滇池流域为例

2013年2月11日Landsat 8在加州范德堡空军基地发射升空,其携带的热红外传感器为反演地表温度提供了一种新的数据,但目前尚没有针对Landsat 8热红外波段反演地表温度的算法。针对Landsat 8第10波段特征,对现有反演地表温度的单窗算法进行了参数修正,得到了用Landsat 8第10波段反演地表温度的单窗算法系数。为了评价修正后算法的精度,用MODTRAN模拟地表温度为20、30和40℃时大气水汽含量分别为1.0、1.5、2.0和2.5g·cm-2传感器高度处的热辐射值,再将模拟数据用修正后的单窗算法反演地表温度,结果表明:地表温度越低、大气水汽含量越低,误差越小;模拟结果的平均误差为0.74℃。说明基于Landsat 8第10波段用修正后的单窗算法反演地表温度是可行的,该方法可为地表温度反演提供一种途径。最后以滇池流域为例,基于2013年4月20日的Landsat 8热红外数据反演了滇池流域的地表温度,并分析了滇池流域地表温度的分布特征。     

一种基于遥感数据快速提取水体信息的新方法

“数字流域”建设已成为水利现代化的重要组成部分。利用遥感技术进行水体信息提取可为构建数字流域提供重要的数据来源。在对Landsat ETM+影像上的水体及其背景地物进行光谱特征分析的基础上,发现水体在近红外和中红外波段（对应于Landsat数据的Band 4、Band 5和Band 7）同时具有强吸收这一典型特征,据此提出了一种新型的水体指数NWI（New Water Index）。其中,将Band 7用于水体指数模型的构建和水体信息的提取目前较少见于文献报道。以厦门岛为研究区,将该指数在经过大气校正的遥感影像上进行实验的结果表明：NWI可用于水体信息的快速有效提取,且具有较高的精度。     

基于ENVISat ASAR影像的湿地植被冠层下淹水范围提取以扎龙保护区为例

以扎龙自然保护区湿地为例,结合ENVISat ASAR多极化(HH/HV)雷达影像与传统的光学影像Landsat TM (band1~5,7),分析雷达影像后向散射系数与Landsat TM影像不同波段反射率在淹水植被、非淹水植被、明水面和裸土不同地表覆被类型的差异。选择训练样本,采用分类回归树(Classification and Regression Tree,CART)模型,分别对两种影像进行分类,可视化表达湿地植被淹水范围空间分布情况。基于实测的植被冠层下淹水范围与非淹水范围样本点对两种数据源的分类结果进行精度验证。结果表明:HH/HV极化影像中,植被覆盖下水体的后向散射系数与其他地表覆被类型有明显区别,分类结果总精度为79.49%,Kappa系数为0.70,湿地植被淹水范围提取精度较高。而TM影像分类结果中,由于部分地区植被覆盖水体,淹水植被分类误差较高。将雷达影像引入沼泽湿地研究,提高了植被淹水范围提取效果,为有效分析湿地生态水文过程提供基础,对湿地水资源合理利用及生物多样性保护具有重要意义。     

基于Radarsat-2全极化数据的高原牧草覆盖地表土壤水分反演

大面积土壤水分反演对于青海湖流域草场的管理和保护具有重要的意义。利用C波段全极化的Radarsat-2 合成孔径雷达(SAR)影像数据,开展了青海湖流域刚察县附近草场的土壤水分反演研究,在“水-云”模型和Chen模型的基础上,发展了一种新的土壤水分反演算法。该算法消除了植被覆盖以及地表粗糙度对雷达后向散射系数的影响。实验结果表明:预测结果能够与实测数据很好地吻合,R²、RMSE和RPD分别达到0.71\,3.77%和1.64,反演精度较高,能够满足研究区土壤水分的反演精度要求。如果能够更细致地刻画植被层以及地表粗糙度对雷达后向散射系数的影响,土壤水分反演精度有望得到进一步提高。
     

一种改进的Canny算子在机器人视觉系统中的应用

在对传统Canny边缘检测算子深入分析的基础上,利用统计原理研究和分析图像灰度值数据,提出一种改进型Canny算子.与传统的Canny边缘检测算子相比,改进型的Canny算子将边缘点邻域内灰度相似的像素点的灰度平均值代替原灰度值,并引进一种自适应的阈值确定方法,根据灰度值信息确定高低阈值,在保持边缘提取准确性的同时增加了双阈值设置的自适应性.最后将改进型的Canny算子应用于工业机器人视觉系统中,并与传统的Canny算子的实验结果进行比对分析.实验结果表明,改进型的Canny算子更好的权衡了噪声和边缘之间的关系,对待测物体的边缘提取效果更好.     

基于航拍图像的电力线自动提取

提出了一种用于从航拍图像中自动提取电力线的方法。该方法首先对图像进行预处理,然后选用Robert算子、Sobel算子、Prewitt算子、Canny算子对预处理后的图像进行边缘检测,并对各种边缘检测的结果进行对比分析,得出canny算子检测到的边缘效果最为理想,最后采用链码跟踪法提取出电力线。实验结果证明该方法可以准确提取一定背景条件下的电力线,可以为电力线的后续处理提供重要的数据。     

利用正演计算方法分析地形和噪音对多波段遥感数据的影响

在没有干扰和噪音影响的理想条件下,多波段遥感数据中不同地物在散点图上的位置是一
个点。但在实际情况中,地形和噪音大大地改变了目标分布形状。利用DEM数字高程数据以及太
阳高度角和方位角等参数计算出地表接收到的辐射能量,然后设置已知目标,利用散点图来分析不
同反射率下目标所占的空间位置。再通过增加不同强度的噪音,分析目标在散点图上的变化规律,
以便寻找目标分离的有效特征。     

Improved direction estimation for Di Zenzo's multichannel image gradient operator

Gradient estimation is one of the most important tasks in image/video processing. For multichannel images, a classical and widely-used gradient method is Di Zenzo's gradient operator, which is based on the measure of squared local contrast variation of multichannel images. However, up to now, the indetermination of Di Zenzo's gradient direction has not been well solved, which results in errors occurring in most of the subsequent studies in which Di Zenzo's vector gradient is used. In this paper, this problem is solved thoroughly. Furthermore, the ranges of the values that the gradient angle should take in various cases are also analyzed. As an application in color image processing, a color version of Canny edge detector is implemented by introducing the new gradient estimator to the traditional grayscale image Canny operator. The experimental results indicate that the improved Di Zenzo's gradient operator is currently one of the best color gradient estimators and outperforms other state-of-the-art color image gradient methods. The improved multichannel gradient operator not only provides accurate gradient estimation but also is efficient and easy to implement.     

Image processing for boundary extraction of remotely sensed data

Image processing algorithms for analysing remotely sensed data are developed. The algorithms proposed in the paper provide means for autoregressive texture modelling and for boundary detection of uniform subimage areas. The boundary detection methods make use of a semicircular entropy operator and of the binary hypothesis testing of the Poisson counting process. The proposed algorithms are applied to the pattern analysis of the isothermal distribution in the oceanic environment.     

Developing a two-step retrieval method for estimating total suspended solid concentration in Chinese turbid inland lakes using Geostationary Ocean Colour Imager (GOCI) imagery

Total suspended solid (TSS) concentration is an important water quality parameter. Mapping its varying distribution using satellite images with high temporal resolution is valuable for studying suspended sediment transportation and diffusion patterns in inland lakes. A total of 255 sites were used to make remote-sensing reflectance measurements and surface water sampling at four Chinese inland lakes, i.e. Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Gorges Reservoir, at different seasons. A two-step retrieval method was then developed to estimate TSS concentration for contrasting Chinese inland lakes, which is described in this article. In the first step, a cluster method was applied for water classification using eight Geostationary Ocean Colour Imager (GOCI) channel reflectance spectra simulated by spectral reflectance measured by an Analytical Spectral Devices (ASD) Inc. spectrometer. This led to the classification of the water into three classes (1, 2, and 3), each with distinct optical characteristics. Based on the water quality, spectral absorption, and reflectance, the optical features in Class 1 were dominated by TSS, while Class 3 was dominated by chl-a and the optical characteristics of Class 2 were dominated jointly by TSS and chl-a. In the second step, class-specific TSS concentration retrieval algorithms were built. We found that the band ratio Band 8/Band 4 was suitable for Class 1, while the band ratio of Band 7/Band 4 was suitable for both Class 2 and Class 3. A comprehensive determination value, combining the spectral angle mapper and Euclidean distance, was adopted to identify the classes of image pixels when the method was applied to a GOCI image. Then, based on the pixel’s class, the class-specific retrieval algorithm was selected for each pixel. The accuracy analysis showed that the performance of this two-step method was improved significantly compared to the unclassed method: the mean absolute percentage error decreased from 38.9% to 24.3% and the root mean square error decreased from 22.1 to 16.5 mg l^–1. Finally, the GOCI image acquired on 13 May 2013 was used as a demonstration to map the TSS concentration in Taihu Lake with a reasonably good accuracy and highly resolved spatial structure pattern.     

基于小波去噪和改进Canny算子的边缘检测

针对传统Canny边缘检测算法在抑制噪声和定位精度方面的不足．提出一种将小波去噪和改进Canny算子相结合的边缘检测方法。采用小波去噪方法对含噪图像进行噪声过滤．再用改进的Canny算子实现边缘检测。仿真实验结果表明,该方法抗噪能力极强,提取的边缘定位精确。     

厦门岛及其邻域海岸线变化的遥感动态监测

利用美国Landsat TM/ETM+卫星影像,分1989、1995和2000年3个时相,研究了福建省厦门市从1989~2000年的岸线变化情况。岸线变化的信息主要通过变化检测和信息提取等一系列遥感技术来获得。研究结果表明,厦门市在所研究的1989~2000年期间,海域面积共减少了7.54 km²,这主要是由于城市用地扩展和养殖业发展占用了海域的面积。分时段研究表明,从1995~2000年,海域减少的面积明显要比1989~1995年期间来的多,这从一个侧面反映了厦门在1995~2000年间,城市建设和经济发展对土地提出了更高的需求。     

基于MPI的遥感影像高效能并行处理方法研究

采用基于不同尺度下的面向特征基元的影像分析方法对高分辨率遥感影像进行基于MPI的处理,即在对常规的影像数据划分方法进行总结分析的基础上,提出了基于特定环境下的非均匀数据划分策略;在进行基于影像数据库的MPI并行处理时,提出了一种新的数据流分配方法。处理结果表明,这两种方法均能够在一定环境下取得比常规方法更高的效率。     

Parallel spatiotemporal autocorrelation and visualization system for large-scale remotely sensed images

Current studies on large-scale remotely sensed images are of great national importance for monitoring and evaluating global climate and ecological changes. In particular, real time distributed high-performance visualization and computation have become indispensable research components in facilitating the extraction of remotely sensed image textures to enable mining spatiotemporal patterns and dynamics of landscapes from massive geo-digital information collected from satellites. Remotely sensed images are usually highly correlated with rich landscape features. By exploiting the structures of these images and extracting their textures, fundamental insights of the landscape can be derived. Furthermore, the interdisciplinary collaboration on the remotely sensed image analysis demands multifarious expertise in a wide spectrum of fields including geography, computer science, and engineering.     

鄱阳湖面积的卫星遥感估计及其与水位关系分析

鄱阳湖水位变幅巨大,近年来旱涝灾害严重,对其进行实时监测具有重要意义。MODIS遥感数据时间分辨率很高,在水体动态监测中具有明显优势。水体提取方法多种多样,在比较不同水体提取方法的基础上,最终选用改进的归一化水体指数(MNDWI)的方法进行鄱阳湖水体提取;利用2000~2010年40期MODIS数据分析了鄱阳湖这10 a的水面面积年际演变特征及季节性变化特征,同时结合同时期的水位数据,建立了鄱阳湖面积-水位关系的模型。与地形图量测结果的对比表明,遥感解译的鄱阳湖水体面积-水位关系与根据地形图分析建立的面积-水位关系具有较好的一致性。     

Remote sensing used to detect moisture status of pecan orchards grown in a desert environment

Remote-sensing techniques can detect and up-scale leaf-level physiological responses to large areas, and provide significant and reliable information on water use and irrigation management. The objectives of this study were to screen leaf-level physiological changes that occur during the cyclic irrigation of pecan orchards to determine which responses best represent changes in moisture status of plants and link plant physiological changes to remotely sensed surface reflectance data derived from the Landsat Thematic Mapper and Enhanced Thematic Mapper Plus (ETM+). The study was conducted simultaneously on two southern New Mexico mature pecan orchards. For both orchards, plant physiological responses and remotely sensed surface reflectance data were collected from trees that were either well watered or in water deficit. Remotely sensed variables included reflectance in band 1, the ratio between shortwave infrared (SWIR) bands (B5:B7), the normalized difference vegetation index, and SWIR moisture indices. Midday stem water potential (Ψ_smd) was the best performing leaf-level physiological response variable for detecting moisture status in pecans. The B5:B7 ratio positively and significantly correlated with Ψ_smd in five of six irrigation cycles while multiple linear regression weighted with six remotely sensed surface reflectance variables revealed a significant relationship with moisture status in all cycles in both orchards (R² > 0.73). Because changes in the B5:B7 band ratio and multiple regression of spectral variables correlate with the moisture status of pecan orchards, we conclude that remotely sensed data hold promise for detecting the moisture status of pecans.     

改进的Beamlet与Canny相结合提取复杂图像线特征

传统Beamlet无结构算法在提取图像线特征时不仅存在重叠模糊的缺陷,而且在提取复杂图像线特征时不能有效地检测出目标信息,细节特征更是难以刻画。针对这些问题,提出将改进的Beamlet无结构算法与Canny算子相结合的方法提取复杂图像的线特征。首先,对图像进行Beamlet变换,通过改进Beamlet无结构算法,采用新的能量统计和制定新的划线规则,以保证每个二进方块最多有一条最优基;然后,对图像用Canny算子检测边缘,通过选取较大的Sigma,只检测明显的大边缘;最后,两者结合得到图像的线特征。从检测的线特征的线型连接程度等方面对该算法的性能进行了评价,并与现有的方法进行了比较,实验结果表明,该方法克服了两种方法单独提取线特征时存在的断裂、重叠、模糊和虚假边缘的缺点,有效地提高了复杂图像线特征提取的准确性和连续性。     

Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland

Hyperspectral remotely sensed data are useful for studying ecosystem processes and patterns. However, spatial characterization of such remotely sensed images is needed to optimize sampling procedures and address scaling issues. We have investigated spatial scaling in ground-based and airborne hyperspectral data for canopy- to watershed-level ecosystem studies of southern California chaparral and grassland vegetation. Three optical reflectance indices, namely, Normalized Difference Vegetation Index (NDVI), Water Band Index (WBI) and Photochemical Reflectance Index (PRI) were used as indicators of biomass, plant water content and photosynthetic activity, respectively. Two geostatistical procedures, the semivariogram and local variance, were used for the spatial scaling analysis of these indices. The results indicate that a pixel size of 6 m or less would be optimal for studying functional properties of southern California grassland and chaparral ecosystems using hyperspectral remote sensing. These results provide a guide for selecting the spatial resolution of future airborne and satellite-based hyperspectral sensors.