基于MODIS地表反射率数据的水体自动提取研究

应用阈值法对遥感图像上的水体目标进行提取时,水陆分割阈值的确定是其难点。以MODIS地表反射率数据为数据源,首先统计大量MODIS地表反射率影像第6波段的水陆分割阈值的范围作为先验阈值范围;然后将历史存档的研究区水体边界矢量叠加到图像上,并且将矢量边界向外扩大一倍,使得扩大后的范围内的水体和陆地面积相当;最后统计扩大后区域的第6波段的灰度直方图,并寻找先验阈值范围内的最小值作为最佳的水陆分割阈值进行水体提取。克服了统计直方图双峰谷值作为分割阈值的传统方法容易受到地物复杂性及噪声影响的难题,使得水陆分割阈值的确定变得更加简单、高效,实现了针对遥感影像上水体目标的自动提取,大大提高了水体提取的效率。     

基于海岸带高光谱成像仪影像的太湖蓝藻水华和水草识别

水华与水草的同步监测对于研究湖泊水环境、生态特性以及水循环具有重要意义,相对于传统监测方法如实地调查,利用遥感手段具有大范围、长时间周期、高效率以及低成本等优势。基于海岸带高光谱成像仪HICO(Hyperspectral Imager for the Coastal Ocean)影像,利用叶绿素a光谱指数和藻蓝蛋白基线的水华和水草识别模型,提取太湖水华和水草分布图,经过检验,水华和水草的平均提取精度为93%和95%;通过水华和水草分布图的叠加分析了2010~2014年太湖水华和水草的分布规律,与相关文献的分析结果一致,进一步证实了识别方法的可靠性;将平均阈值与最佳阈值进行对比分析,提取水华与水草面积的精度分别为75.7%和84%,在对精度要求不高但对效率要求较高的情况下,可以利用平均阈值提取水华和水草,便于实现水华和水草的自动化提取及批处理。     

基于红外图像的桥梁目标自动识别方法

对桥梁的识别研究,在军事上和民用上都具有很重要的意义,国内外与此相关的研究比较多,取得了一定效果,但也存在适用范围不广,处理速度较慢等问题。本文基于红外图像的特性,运用迭代法选取阈值进行阈值分割,得到二值化图像;然后用形态学的方法粗略提取目标点;利用桥梁目标的直线特性,运用Radon变换在图像中提取直线并连接。本算法对水域上的红外图像中的桥梁识别具有通用性,方法直观简洁,计算速度快。     

基于颜色曲线分析的快速焊缝条纹提取方法

针对彩色图像中焊缝条纹提取问题,提出一种基于颜色曲线分析的彩色图像分割方法。该方法首先将原始彩色图像转换到YUV颜色空间,对V通道进行颜色曲线分析,得到一个合适的二值化阈值;接着利用该阈值对V通道图像进行二值化,经形态学去噪和合并离散块处理,得到图像分割模板;最后将其运用到原始图像上,实现对原始彩色图像的焊缝条纹提取。将该方法应用到彩色图像焊缝条纹提取中,对于800×600的彩色图像,平均处理时间约0.3 s,与人工标注结果比较,提取框重叠比例70%以上的占90%,验证了该方法的高效性和准确性。     

焊缝X射线图像缺陷的自动提取与分割

根据薄壁焊缝X射线图像的特点,本文针对基于数学形态法的图像分割技术进行了改进.首先利用数学形态学选取适当的结构元素模拟图像背景,然后利用数字剪影法提取缺陷目标.文章将使用较好的几种阈值化方法进行分析,比较各种方法的基本思想,优缺点及使用范围,最终提出选择迭代分割方法得到图像最佳阈值将图像二值化.实验结果表明.针对不同的缺陷均能得到轮廓清晰的分割效果,为缺陷特征参数的提取和识别的实现打下坚实的基础.     

基于改进分水岭变换的矿石图像分割算法

为了准确测量传送带上的矿石尺寸,提出了一种局部自适应阈值化和改进的分水岭变换相结合的矿石图像分割算法.该算法利用基于积分图像的自适应阈值化算法提取矿石区域;对二值图像做距离变换与双边滤波处理,并应用提出的基于区域合并的分水岭变换算法对图像进行分割;将提取的矿石区域与分割结果进行合并,得到最终的分割结果.对现场采集的复杂的矿石图像进行仿真实验,实验结果表明,该算法分割准确、速度快、光照自适应强.     

基于纹理特征的高分辨率SAR 影像居民区提取

利用灰度共生矩阵计算高分辨率SAR 图像的纹理特征, 通过统计分析选取合适的特征矢量,并基于非监督聚类分析提取居民区。对提取的居民区以一定的面积阈值剔除噪声(细小区域) , 并利用形态学算子对提取边界进行适当的归整, 得到最终结果。在对应的光学图像上人工提取居民区范围, 以此作为实验结果的评价标准。实验结果表明本方法可以得到较好的效果。
     

利用MOOIS监测沙尘暴的影响范围

采用EOS-Terra/MODIS数据对内蒙古的沙尘暴进行监测研究.通过分析沙尘暴的波谱特征和MODIS传感器通道的特点,采取基于双通道阈值的叠加分析法对沙尘暴进行提取监测.结果表明基于双通道阈值的叠加分析法有利于对沙尘暴信息的准确提取,为利用MODIS数据进行沙尘暴监测提供了有效手段.     

利用视觉显著性的图像分割方法

提出一种利用视觉显著性对图像进行分割的方法。首先提取图像的底层视觉特征,从局部显著性、全局显著性和稀少性3个方面计算各特征图像中各像素的视觉显著性,得到各特征显著图;对各特征显著图进行综合,生成最终的综合显著图。然后对综合显著图进行阈值分割,得到二值图像,将二值图像与原始图像叠加,将前景和背景分离,得到图像分割结果。在多幅自然图像上进行实验验证,并给出相应的实验结果和分析。实验结果表明,该方法正确有效,具有和人类视觉特性相符合的分割效果。     

基于MODIS的大气因素对蓝藻水华信息提取的影响

本文利用MODIS提供的两种影像产品和代表大气条件的气溶胶产品,运用单波段(NIR)和比值植被指数(NIR/G)两种方法分别提取太湖蓝藻信息.在阐述气溶胶的光学厚度对遥感影像影响的基础上,定量分析了气溶胶光学厚度对两种方法提取蓝藻水华面积的净变化值和选取阈值的影响程度.研究结果表明,运用单波段和比值植被指数两种方法提取太湖蓝藻水华面积差异与气溶胶光学厚度的相关性,固定阈值情况下的相关性高于自选阈值中的对应值.两种指数中大气校正前的阈值与气溶胶光学厚度的相关性都要高于大气校正后两者的相关性.这说明大气会对阈值的设定产生一定的影响,进而影响蓝藻信息提取精度.因此,定量分析气溶胶光学厚度对监测蓝藻是至关重要的.     

Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation

A massive floating green macroalgae bloom (GMB) has occurred for several years consecutively in the Yellow Sea since 2007. In view of the rapid growth of green macroalgae, early detection of its patches at first appearance by satellite imagery is of importance, and the central issue is the selection of appropriate satellite data. As a first step towards this goal, based on quasi-synchronous satellite images of HJ-1A/B (China Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting) charge-coupled devices (CCDs), Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) and TERRA Moderate Resolution Imaging Spectroradiometer (MODIS), GMB monitoring abilities by these data were compared. The average percentage difference (APD) of the GMB areas derived by ASAR and CCD was less than 15%, which may be partly attributed to the inability of synthetic aperture radar (SAR) data to detect macroalgae suspended beneath the sea surface. The macroalgae area extracted by MODIS was over two times of that extracted by CCD, which was mainly explained by the difference in their spatial resolutions (250 vs 30 m). The effects of the configuration of sensor bands and the aerosol optical properties on the comparison result were found to be negligible, and the underlying reason is analysed by atmosphere radiative transfer modelling. With satellite images, the drifting velocity of macroalgae patches was estimated to be about 0.21 m s^–1, which was in agreement with the surface current field numerically simulated by the Hybrid Coordinate Ocean Model (HYCOM). It indicates that numerical modelling can aid in deduction of the situation of the patches when satellite data are not available, and on the other hand, satellite data can be used to estimate sea-surface currents through monitoring the movement of green algae. By a comprehensive comparison of available satellite data in operation, for the early detection of macroalgae patches and warning of a massive bloom, CCD data from the HJ-1A/B constellation was preferred, with 30 m spatial resolution, 700 km swath width and 2 day revisiting period. SAR data may be an effective supplement, which can avoid the effects of bad weather (cloud, fog and haze) on optical satellite monitoring.     

ENVISAT ASAR影像地理定位方法

随着ENVISAT ASAR传感器成功通过调试阶段，国内将有越来越多的用户能够获取到ASAR数据。如何进行ASAR数据的地理定位是实际应用中需要解决的第一个关键问题。基于距离一多普勒(RD)模型发展了对ASAR主要影像产品进行地理定位的方法，重点解决了不同影像产品类型距离向方程的构建方法：对于斜距产品，距离向方程由第一斜距和影像距离向像元大小所确定的直线方程描述；精确地距模式影像元数据中仅提供一套SRGR参数，可直接用于建立距离向方程；而中分辨率地距模式影像在元数据中提供了若干套SRGR参数，需根据方位向成像时间与SRGR参数的方位向更新时间的关系，通过插值方法建立距离向方程。利用该方法对APP_1P、APM_1P、APS_1P、IMP_1P和IMM_1P共5景ASAR数据进行了地理定位试验。SAR处理器在生成ASAR数据产品时，已经对影像4个角点的地理位置进行了定位，并将定位结果记录在ASAR数据的元数据中。本文以这些定位结果为基准对所发展的定位方法的相对精度进行了评价，实验结果表明：对于APP_1P、APS_1P和IMP_1P数据产品，经度和纬度相对误差都小于1m；对于中分辨率APM_1P和IMM_1P数据产品，最大经度误差为59．73m，最大纬度误差为83．38m，分别是影像像元大小的0．8倍和1．1倍；总之，本文定位结果和生产这些产品的SAR处理器的定位结果有很高的符合程度，对进一步发展ASAR数据正射校正算法及其他相关雷达数据处理技术有一定的参考意义。     

A Review of: “Exploration of the Solar System by Infrared Remote Sensing”. By R. A. HANEL,B. J. CONRATH,D. E. JENNINGS and R. E. SAMUELSON. (Cambridge: Cambridge University Press, 1992.) [Pp. 458.] Price £75.00.

Crop classification is a key issue for agricultural monitoring using remote-sensing techniques. Synthetic aperture radar (SAR) data are attractive for crop classification because of their all-weather, all-day imaging capability. The objective of this study is to investigate the capability of SAR data for crop classification in the North China Plain. Multi-temporal Envisat advanced synthetic aperture radar (ASAR) and TerraSAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using different combinations of these SAR data and texture features. The results indicated that multi-configuration SAR data achieved satisfactory classification accuracy (best overall accuracy of 91.83%) in the North China Plain. ASAR performed slightly better than TerraSAR data acquired in the same time span for crop classification, while the combination of two frequencies of SAR data (C- and X-band) was better than the multi-temporal C-band data. Two temporal ASAR data acquired in late jointing and flowering periods achieved sufficient classification accuracy, and adding data to the early jointing period had little effect on improving classification accuracy. In addition, texture features of SAR data were also useful for improving classification accuracy. SAR data have considerable potential for agricultural monitoring and can become a suitable complementary data source to optical data.     

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models

A major source of error for repeat‐pass Interferometric Synthetic Aperture Radar (InSAR) is the phase delay in radio signal propagation through the atmosphere (especially the part due to tropospheric water vapour). Based on experience with the Global Positioning System (GPS)/Moderate Resolution Imaging Spectroradiometer (MODIS) integrated model and the Medium Resolution Imaging Spectrometer (MERIS) correction model, two new advanced InSAR water vapour correction models are demonstrated using both MERIS and MODIS data: (1) the MERIS/MODIS combination correction model (MMCC); and (2) the MERIS/MODIS stacked correction model (MMSC). The applications of both the MMCC and MMSC models to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the Southern California Integrated GPS Network (SCIGN) region showed a significant reduction in water vapour effects on ASAR interferograms, with the root mean square (RMS) differences between GPS‐ and InSAR‐derived range changes in the line‐of‐sight (LOS) direction decreasing from ～10 mm before correction to ～5 mm after correction, which is similar to the GPS/MODIS integrated and MERIS correction models. It is expected that these two advanced water vapour correction models can expand the application of MERIS and MODIS data for InSAR atmospheric correction. A simple but effective approach has been developed to destripe Terra MODIS images contaminated by radiometric calibration errors. Another two limiting factors on the MMCC and MMSC models have also been investigated in this paper: (1) the impact of the time difference between MODIS and SAR data; and (2) the frequency of cloud‐free conditions at the global scale.     

Integration of multitemporal/polarization C‐band SAR data sets for land‐cover classification

This paper investigates the potential of multitemporal/polarization C‐band SAR data for land‐cover classification. Multitemporal Radarsat‐1 data with HH polarization and ENVISAT ASAR data with VV polarization acquired in the Yedang plain, Korea are used for the classification of typical five land‐cover classes in an agricultural area. The presented methodologies consist of two analytical stages: one for feature extraction and the other for classification based on the combination of features. Both a traditional SAR signal property analysis‐based approach and principal‐component analysis (PCA) are applied in the feature extraction stage. Special concerns are in the interpretation of each principal component by using principal‐component loading. The tau model applied as a decision‐level fusion methodology can provide a formal framework in which the posteriori probabilities derived from different sensor data can be combined. From the case study results, the combination of PCA‐based features showed improved classification accuracy for both Radarsat‐1 and ENVISAT ASAR data, as compared with the traditional SAR signal property analysis‐based approach. The integration of PCA‐based features based on multiple polarization (i.e. HH from Radarsat‐1, and both VV and VH from ENVISAT ASAR) and different incidence angles contributed to a significant improvement of discrimination capability for dry fields which could not be properly classified by using only Radarsat‐1 or ENVISAT ASAR data, and thus showed the best classification accuracy. The results of this case study indicate that the use of multiple polarization SAR data with a proper feature extraction stage would improve classification accuracy in multitemporal SAR data classification, although further consideration should be given to the polarization and incidence angle dependency of complex land‐cover classes through more experiments.     

Envisat-ASAR数据的特点及其在多云多雨地区的应用前景

Envisat是由欧空局发射的一颗先进的极轨对地观测卫星,载有10种传感器,其中有先进的合成孔径雷达ASAR(Advanced Synthetic Aperture Radar)。ASAR工作在C波段,具有主动相控天线系统,5种成像模式,7种成像条带及交替极化成像功能。以获得的广东肇庆地区的ASAR交替极化模式精确分辨率图像为实例,介绍了ASAR数据的特点,分析ASAR图像中建筑物、河流、农田、船舶、林地等几种典型地物的后向散射系数值。结果表明ASAR数据可以广泛应用于多云多雨地区的土地覆盖分类,农作物估产,船只探测和海洋等领域。     

Analysis of ENVISAT ASAR data for forest parameter retrieval and forest type classification—a case study over deciduous forests of central India

The present paper gives an account of potential of Environment Satellite‐Advanced Synthetic Aperture Radar (ENVISAT‐ASAR) C‐band data in forest parameter retrieval and forest type classification over deciduous forests of Tadoba Andhari Tiger Reserve (TATR), central India. Ground data on phyto‐sociology and Leaf Area Index (LAI) over the study area was collected in 23 sampling points (20m×20m) over the study area. Phyto‐sociological data collected over the study area was used to compute plot‐wise biometric parameters like basal area, volume, stem density and dominant height. ENVISAT ASAR data covering the study area, pertaining to 24 November 2005, has been geo‐referenced and digital number (DN) values were converted to radar backscatter values. Regression analysis between backscatter and the retrieved biometric variables has been done to explain the relationships between SAR backscatter and forest parameters. Analysis showed a significant correlation between backscatter and biometric parameters and backscatter values typically increased with increase in basal area, volume, stem density and dominant height. The scatter observed between ASAR backscatter and stem density, basal area and dominant height suggested limitation of C‐band data in estimating biometric variables in heterogeneous forest systems. Further, ASAR data was used in conjunction with Indian Remote sensing Satellite (IRS‐P6)—Linear Imaging Self Scanner (LISS) III data of 16 October 2004 to classify the study area into different land use/land cover (LU/LC) classes. Various texture and adaptive filters were applied on ASAR image to reduce speckle noise and enhance image features. An attempt is made to merge ASAR image with LISS‐III to enhance feature discrimination. Training sets corresponding to the ground data have been used to derive confusion matrices for the ASAR and LISS‐III images. Results suggested better discrimination of vegetation types in the merged data suggesting the possible use of ASAR data in forest type discrimination.     

应用MODIS数据监测巢湖蓝藻水华的研究

以巢湖为研究区域,以MODIS 卫星影像为数据源,结合准同步的地面水质监测数据,将MODIS 250 m分辨率的波段反射率与叶绿素a浓度实测值进行相关分析。在此基础上通过回归拟合,构建基于中分辨率成像光谱仪(MODIS) 的叶绿素遥感提取模型。应用模型成功提取出蓝藻爆发水域chl-a的分布。从MODIS遥感图像上可以清晰地反映出巢湖这次蓝藻爆发的强度、地点和分布范围 。研究结果表明:用MODIS影像监测巢湖蓝藻水华是可行的,其中250m分辨率波段1 、2的比值组合r2/r1与叶绿素a浓度实测值高度相关(R=0.909 3),适于反演叶绿素a浓度。     

ENVISAT-ASAR数据处理介绍

ASAR(Advanced Synthetic Aperture Radar)作为当今最先进的合成孔径雷达数据于2006年1月在国内实现共享,由于其处理困难限制了它在国内的应用推广.本文介绍了欧空局开发的雷达处理工具包软件BEST(Basic Envisat Sar Toolbox),该软件可以实现某些雷达数据处理功能,具有简单实用的特点.并且使用ASAR WSM数据与MODIS图像进行了融合.作为国内率先共享的两种数据,ASAR与MODIS组合使用将会发挥更大的作用.     

A practical approach to retrieving the finer areas of algal bloom in inland lakes from coarse spatial resolution satellite data

Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) images have been used to monitor algal blooms in the open ocean, coastal waters, and inland lakes. However, it is difficult to obtain an accurate definition of algal bloom areas in inland lakes due to the spatial resolution of the generated images. This study developed a practical approach that uses a linear spectral mixing model with a moving window (LSMM), to obtain a finer algal bloom area. The approach analyses the differences in areas of algal bloom retrieved from MODIS images with 250 and 500 m spatial resolutions from 2012 to 2015 and synchronous VIIRS images with 750 m spatial resolution. Forty-two data sets with 126 satellite images were selected. The results showed that the average relative area difference (RAD) of algal bloom in the MODIS 500 m image was approximately 21.31% compared with the MODIS 250 m image and approximately 33.77% compared with the VIIRS image. A 5 × 5 window size was selected for the MODIS 500 m and VIIRS images. The results demonstrated that the approach can be successfully applied to MODIS 500 m and VIIRS images because the RAD significantly improved. The average RAD decreased to 9.39% in the MODIS 500 m image and to 12.84% in the VIIRS image. The relationship between the landscape of the algal bloom patch and the RAD showed that the performance of the LSMM method improved as the patch density (PD) increased from 0 to 2. When the perimeter-area ratio (PARA) is greater than 2 and the mean patch size (MPS) is less than approximately 5 km², the LSMM method significantly improved the RAD. An independent validation demonstrated that the LSMM method developed for MODIS and VIIRS images can be successfully applied to other coarser-resolution spatial imageries such as Geostationary Ocean Color Imager (GOCI) images. The LSMM method is more effective than the other methods for determining the fragmented landscape of algal blooms.