基于HJ-1卫星CCD遥感数据的作物秸秆光谱识别模型与提取方法研究

作物秸秆信息的准确提取对于农业可持续发展与秸秆焚烧火点的探测有重要意义。本文在野外作物秸秆光谱采集与光谱特征分析的基础上,以江苏省扬州市区为典型研究区,依照HJ卫星CCD相机多光谱遥感波段进行光谱重采样,并结合HJ卫星CCD遥感数据,建立了作物秸秆光谱识别乘积指数(SMI),最后通过改进PSO最大类间方差算法对SMI影像进行"动态-全局"阈值分割,实现了作物秸秆信息的有效提取。     

HJ-1A/1B星CCD传感器数据在黄东海浒苔监测中的应用

基于浒苔光谱特性和归一化植被指数,利用HJ-1A/1B星CCD传感器数据对黄东海浒苔进行监测。监测结果表明HJ-1A/1B星CCD传感器,可以提供比MODIS更多的精细信息,如提供重点海域的浒苔分布范围、覆盖范围、变化状况等信息。     

HJ-1星CCD相机场地辐射定标与真实性检验研究

为了满足HJ-1星遥感数据定量化应用的需求,2008年10月中旬,以敦煌定标实验场为地面目标,采用反射率基法对HJ-1A和HJ-1B星的CCD相机开展场地绝对辐射定标,以确定CCD相机响应的变化,并对得到的定标结果进行了误差分析。同时利用非定标同日的敦煌场和贡格尔实验场的CCD星地同步数据得到的表观辐亮度作为标准值,对定标系数进行真实性检验研究。结果表明,定标系数模拟出的CCD表观辐亮度与标准值非常接近,误差均小于3%。研究表明:此次定标实验的结果精度高,并将两次同步数据的定标系数平均值作为定标系数结果,为CCD相机定量化应用研究奠定了良好基础。     

基于HJ-1B卫星遥感数据的水稻识别技术研究

为快速、准确地在遥感图像上识别水稻作物的信息,满足县级尺度水稻遥感监测的需要,以野外实地调查资料、1∶5万地形图数据为辅助,通过光谱分析法,分析研究HJ-1B星CCD数据的水稻作物的光谱反射特性,建立水稻作物遥感信息识别模型。采用决策树分类方法提取水稻作物信息,并将该技术方法应用于广西宾阳县水稻作物信息提取研究。采用实测样地数据,利用混淆矩阵进行精度评价验证,总精度为94.9%,Kappa系数为0.8533。研究表明,该水稻作物的识别技术,可以为了解我国水稻种植情况,进行水稻长势监测和产量估测提供技术参考。     

基于HJ-1卫星CCD数据的厦门海域叶绿素a浓度反演

针对HJ-1A/B卫星CCD数据,建立适合于厦门海域的叶绿素a浓度反演模型,将为持续监测该海域的赤潮提供时间序列的叶绿素a浓度数据。基于2013年7月31日厦门海域水体实测光谱与叶绿素a浓度同步测量数据,及HJ\|1B卫星CCD2光谱响应函数,对各波段遥感反射率与叶绿素a浓度的相关性进行比较,证实蓝、绿波段比值与叶绿素a浓度相关性最高。对OC3模型在内的5种模型的反演结果和实测叶绿素a浓度做相关性分析,发现各模型相关系数均达到0.7以上。利用2013年7月30日实测数据对同期厦门海域HJ-1B卫星CCD2数据叶绿素a浓度反演结果进行精度验证,结果表明本地化的10指数模型在反演叶绿素a浓度动态范围较大的区域具有更高的精度。     

HJ-1 CCD图像自动几何精纠正系统的设计与实现

由于环境与灾害监测预报小卫星HJ-1A/1B星CCD数据幅宽大,时间分辨率高,数据量大,实现一个自动几何精纠正系统有迫切的现实意义。描述一个HJ-1 CCD图像自动几何精纠正系统的框架设计和集成实现方案,包括系统功能组成、工作流程和模块集成方法以及系统的具体实现过程和实验结果的分析。系统采用全球或局部区域的经过地理编码的TM数据作为基准图像,将图像精纠正问题转化为图像自动配准的问题,同时通过任务流程管理实现图像的批量处理功能。     

HJ-1星多光谱影像火烧迹地信息提取研究——以百色市为例

为识别火烧迹地等地类,以广西百色市为研究区,采用HJ-1星多光谱影像数据近红外波段光谱值、林火发生前后两时相各自NDVI值以及NDVI变化值,基于先验知识和统计分析构建决策树分类模型,通过与传统最大似然分类提取结果的比较分析,表明基于多特征的决策树模型能够有效地对HJ-1星多光谱遥感数据进行火烧迹地等地类提取,在研究区并具有良好的推广性。     

基于Terra/MODIS数据的HJ-1B/CCD1交叉定标方法研究

交叉辐射定标是国际上新近发展起来的一种无场地定标方法,它的应用弥补了场地定标成本较高、定标参数更新周期较长的不足。对于我国2008年发射的环境与灾害监测预报小卫星CCD数据而言,探索交叉辐射定标方法的适用性,对及时发现传感器辐射性能的变化,促进CCD遥感数据的定量化应用具有重要意义。本研究以辐射定标精度较高的Terra/MODIS数据为参考,分别使用光线匹配法（RM）和辐射传输模型方法（RTM）对HJ-1B/CCD1数据进行交叉辐射定标,并与相同条件下进行的场地定标结果比较。实验结果表明,使用这两种方法获取的CCD1的第2、3、4波段的定标结果与场地定标结果差异较小,只有第1波段定标结果与场地定标结果差异相对较大,这证明了交叉辐射定标方法的有效性。另外,虽然RTM方法考虑了参考传感器和待定标传感器光谱响应和观测几何的差异,但是由于RTM方法会受到所使用的6S模型本身的误差以及输入的大气参数、地表参数测量误差的影响,该方法并不总是优于RM方法。     

基于HJ-1 CCD数据的洪涝灾害范围动态监测研究——以黑龙江省抚远县为例

洪涝灾害范围是表征洪涝灾害时空分布特征的关键物理参数,是洪涝灾害灾情评估的主要内容和重要基础。充分利用遥感技术、地面站网观测数据和实地调查核查等手段,是当前"天、空、地、现场"一体化开展洪涝灾害范围动态监测的研究前沿。针对洪涝灾害范围实时动态演变过程监测时效性受数据和信息提取效率制约的问题,提出了基于HJ-1CCD数据利用区域生长算法的洪涝灾害范围提取模型。在研究多种地物目标的影像特征与光谱响应特征基础上,采用区域生长算法对2013年汛期黑龙江省抚远县洪涝灾害范围进行识别和判定。结果表明:汛期内稳定高频次获取的HJ-1CCD数据能为洪涝灾害范围识别和动态演变过程监测提供数据保障。区域生长法提取集中连片水体的效率高,能够将过去目视解译、人工勾画需要5h的工作量缩短到10min之内;提取结果宏观性强,在影响范围大的洪涝灾害范围动态监测业务中发挥着重要的作用。     

基于HJ-1A/B卫星CCD数据的土地宏观监测试验研究

对HJ-1A/B卫星CCD数据质量进行分析,并采用人工解译和计算机自动分类两种方法进行土地利用信息提取试验研究。结果表明,HJ-1A/B卫星CCD数据能够满足制作1∶100 000比例尺数字正射影像图的平面精度要求,可用于不大于1∶100 000比例尺的土地利用宏观遥感监测。
     

HJ-1A/B卫星CCD影像大气订正

大气订正是遥感信息定量化研究中必不可少的一步,目前已有一些成熟的方法,但由于HJ-1A/B卫星CCD相机波段设置特点,常规的大气订正方法基本不适合于HJ-1A/BCCD影像.本文在大量分析HJ-1A/BCCD影像中不同地物的多种指数基础上,提出了改进暗目标法实现HJ-1A/BCCD影像的大气订正,该方法采用比值植被指数(RVI)、土壤调整植被指数(SAVI)和归一化水体指数(NDWI)的综合分析法确定暗像元自动提取,使之适用于环境减灾卫星CCD影像数据.为了客观地验证该方法的精度,本文选取地表平坦均一的敦煌校正场作为实验区,通过多次测量卫星过境时的地表反射率进行分析验证.     

一种卫星遥感影像村庄区域提取算法

卫星遥感影像提取村庄区域在地理和气象领域均有十分重要的意义.针对卫星遥感影像的特点,提出了一种村庄区域提取方法.利用改进的去雾算法对卫星遥感影像进行预处理,通过遥感卫星影像的颜色特征实现分割,结合村庄区域分布特点进行去噪处理,实现卫星遥感影像村庄区域的提取.实验结果表明:该算法能够对卫星遥感图像中不同类型村庄区域进行提取,且提取准确率高,可以应用于地理以及气象等领域.     

Using satellite remote sensing for DEM extraction in complex mountainous terrain: Landscape analysis of the Makalu Barun National Park of eastern Nepal

The design and management of national parks and other protected areas requires a broad base of physiographic and geo-ecological information about the landscape. This paper evaluates the effectiveness of satellite remote sensing for photogrammetric stereo-mapping and digital elevation model (DEM) extraction within remote mountainous terrain. As a case study, a landscape analysis of the Makalu Barun National Park and Conservation Area of east Nepal (27.5° N, 87.0° E) was examined. The study area is a highly complex and rugged mountain landscape, with extreme topographic relief and an elevation gradient spanning more than 8300 m. A DEM extracted from stereo SPOT imagery resulted in a median disagreement of 58 m when compared to a DEM generated from a conventionally digitized GIS dataset of topographic contours (scale=1:250 000). Visual comparison of the two DEMs showed substantial agreement at the landscape scale, while larger scale comparison of 100 m contours revealed some localized differences. The SPOT extracted DEM provided equal or better basis for orthorectification of satellite imagery when compared to the conventional DEM. Derivative landscape analysis outputs, such as hydrological modelling, drainage networks and watershed boundaries, compared well with results based upon the conventional dataset. Intermediate map products useful for field research and mapping included production of an orthorectified satellite base-map image. Additionally, a fused multisensor high resolution image of the study area, combining Landsat Thematic Mapper (TM) and SPOT imagery at 10 m resolution, was orthorectified to produce a false-colour satellite image map highlighting the spectral discrimination between land cover classes.     

Detection limits of coral reef bleaching by satellite remote sensing: Simulation and data analysis

Monitoring of coral reef bleaching has hitherto been based on regional-scale, in situ data. Larger-scale trends, however, must be determined using satellite-based observations. Using both a radiative transfer simulation and an analysis of multitemporal Landsat TM images, the ability of satellite remote sensing to detect and monitor coral reef bleaching is examined. The radiative transfer simulation indicates that the blue and green bands of Landsat TM can detect bleaching if at least 23% of the coral surface in a pixel has been bleached, assuming a Landsat TM pixel with a resolution of 30×30 m on shallow (less than 3 m deep) reef flats at Ishigaki Island, Japan. Assuming an area with an initial coral coverage of 100% and in which all corals became completely bleached, the bleaching could be detected at a depth of up to 17 m. The difference in reflectance of shallow sand and corals is compared by examining multitemporal Landsat TM images at Ishigaki Island, after normalizing for variations in atmospheric conditions, incident light, water depth, and the sensor's reaction to the radiance received. After the normalization, a severe bleaching event when 25-55% of coral coverage was bleached was detected, but a slight bleaching event when 15% of coral coverage was bleached was not detected. The simulation and data analysis agreed well with each other, and identified reliable limits for satellite remote sensing for detecting coral reef bleaching. Sensitivity analysis on solar zenith angle, aerosol (visibility) and water quality (Chl a concentration) quantified the effect of these factors on bleaching detection, and thus served as general guidelines for detecting coral reef bleaching. Spatial misregistration resulted in a high degree of uncertainty in the detection of changes at the edges of coral patches mainly because of the low (∼30 m) spatial resolution of Landsat TM, indicating that detection of coral reef bleaching by Landsat TM is limited to extremely severe cases on a large homogeneous coral patch and shallow water depths. Satellite remote sensing of coral reef bleaching should be encouraged, however, because the development and deployment of advanced satellite sensors with high spatial resolution continue to progress.     

可逆数据隐藏算法在卫星遥感图像上的应用

针对卫星遥感图像受环境因素影响导致图像对比度下降,以及现有可逆信息隐藏算法嵌入容量低等问题,提出一种新的可逆数据隐藏算法,实现在遥感图像安全传输的同时增强图像的对比度.算法为了提高嵌入率,改进已有算法的嵌入方式,同时在信息嵌入的过程中,逐步增强卫星遥感图像的对比度.实验结果表明:提出的算法和一些典型可逆数据隐藏.     

A spatio-temporal fusion method for remote sensing data Using a linear injection model and local neighbourhood information

This paper presents a spatio-temporal fusion method for remote sensing images by using a linear injection model and local neighbourhood information. In this method, the linear injection model is first introduced to generate an initial fused image, the spatial details are extracted from the fine-resolution image at the base date, and are weighted by a proper injection gains. Then, the spatial details and the relative spectral information from the coarse-resolution images are blended to generate the fusion result. To further enhance its robustness to the noise, the local neighbourhood information, derived from the fine-resolution image and the fused result simultaneously, is introduced to refine the initial fused image to obtain a more accurate prediction result. The algorithm can effectively capture phenology change or land-cover-type change with minimum input data. Simulated data and two types of real satellite images with seasonal changes and land-cover-type changes are employed to test the performance of the proposed method. Compared with a spatial and temporal adaptive reflectance fusion model (STARFM) and a flexible spatio-temporal fusion algorithm (FSDAF), results show that the proposed approach improves the accuracy of fused images in phenology change area and effectively captures land-cover-type reflectance changes.     

High-precision extraction of nearshore green tides using satellite remote sensing data of the Yellow Sea,China

Since 2007, a massive floating green tide has occurred repeatedly in the Yellow Sea. The work involved in monitoring and clearing such a phenomenon requires highly precise spatial information. Satellite remote sensing data can provide such detail but they can be limited by long revisit periods and small-scale observational coverage. Therefore, in operational monitoring of green tides, Moderate Resolution Imaging Spectroradiometer (MODIS) data have an important role because of their short revisit period (twice daily) and large-scale observational coverage (viewing swath width: 2330 km). However, errors exist in MODIS-derived areal detection of green tides because its coarse spatial resolution (250–1000 m) can lead to mixed-pixel problems (i.e. in many cases, the limit of spatial resolution results in single pixels covering multiple ground objects). In this study, a method for spectral unmixing was developed for remote green tide detection. This method is based on mathematical morphology, and it integrates both the spatial and spectral characteristics of the image in the process of endmember extraction. Experiments using simulated and actual MODIS images were undertaken to illustrate the advantages of the proposed method. Compared with traditional linear unmixing methods, the abundance and area of green tides extracted by the proposed method were closer to the ‘real’ condition. The average relative deviation of green tide area decreased from 84.0% to 21.0%. The results illustrate that the proposed method is able to work efficiently and that it can provide improved information on green tide abundance and area.     

遥感图像中油罐目标精确定位与参数提取

目的 浮动顶油罐是遥感图像中具有圆形特征的典型人造目标,其高精度定位与参数提取问题是一类代表性的应用问题,针对该问题,传统的基于圆形特征的变换域提取方法鲁棒性差,参数选择需要不断手动调整;基于深度学习的方法利用对已有标注图像的训练求解网络参数,提高了自动化程度,但对于圆形目标而言,覆盖圆周需要较大的感受野,这对应较大的网络结构,随之带来细节信息缺失或参数量、运算量增大的问题。本文针对油罐的定位与参数提取问题,将传统特征提取与深度学习结合,提出了一种计算量小、精度高的方法。方法 基于快速径向对称变换（fast radial symmetry transform,FRST）后的变换域数据及原始数据构建了卷积神经网络（convolutional neural networks,CNN）,给出了训练过程及参数选择,有效地将圆形特征的先验引入深度学习过程,计算复杂度低,用较少层的网络实现了高精度的定位。结果 基于SkySat数据的实验表明,该方法比单纯基于深度学习的方法在相同网络量级上精度得到了有效提高,预测误差平均降低了17.42%,且随着网络深度的增加,精度仍有明显提高,在较浅层次网络中,预测误差平均降低了19.19%,在较深层次网络中,预测误差平均降低了15.66%。结论 本文针对油罐遥感图像定位与参数提取问题,提出了一种基于变换域特征结合深度学习的方法,有效降低了计算量,提升了精度和稳定性。本文方法适用于油罐等圆形或类圆形目标的精确定位和参数提取。     

Pan-sharpening based on common saliency feature analysis and multiscale spatial information extraction for multiple remote sensing images

ABSTRACT

The requirements of spectral and spatial quality differ from region to region in remote sensing images. The employment of saliency in pan-sharpening methods is an effective approach to fulfil this kind of demands. Common saliency feature analysis, which considers the mutual information between multiple images, can ensure the consistency and accuracy when assigning saliency to regions in different images. Thus, we propose a pan-sharpening method based on common saliency feature analysis and multiscale spatial information extraction for multiple remote sensing images. Firstly, we extract spatial information by the guided filter and accurate intensity component estimation. Then, a common saliency feature analysis method based on global contrast calculation and intensity feature extraction is designed to obtain preliminary pixel-wise saliency estimation, which is subsequently integrated with text-featured based compensation to generate adaptive injection gains. The introduction of common saliency feature analysis guarantees that the same pan-sharpening strategy will be applied to regions with similar features in multiple images. Finally, the injection gains are used to implement the detail injection. Our proposal satisfies diverse needs of spatial and spectral information for different regions in the single image and guarantees that regions with similar features in different images are treated consistently in the process of pan-sharpening. Both visual and quantitative results demonstrate that our method has better performance in guaranteeing consistency in multiple images, improving spatial quality and preserving spectral fidelity.     

Assumptions implicit in remote sensing data acquisition and analysis

Abstract

The literature contains many examples of image acquisition and analysis which have been inappropriately applied and which have led to empirical results which may not be reproducible, or which are not conclusive. In this paper, we deal with eleven major assumptions which are implicit in the acquisition and in the analysis of passively sensed digital image data. It is hoped that an enumeration of such assumptions might lead to improved rules for image acquisition and analysis.