一种反射光谱耦合的遥感自然色影像模拟方法

针对多光谱遥感影像像元或光谱库地物样本进行多光谱谱间关系耦合模拟时通常会存在样本与目标影像时相不一致、样本受大气干扰等引起蓝波段及自然色模拟模型不稳定、精度难以提高的问题,提出一种利用MODIS地表反射率产品(MOD09)作为参考影像选取样本进行光谱耦合的自然色模拟方法。首先对MOD09选取的样本利用非线性回归模型——BP神经网络来拟合MOD09蓝波段与红、绿、近红波段间光谱关系;然后,基于这种拟合关系,通过已有的绿、红、近红波段来模拟其他多谱遥感影像蓝光波段,并合成其自然色影像。实验结果表明,BP神经网络在很好地模拟出多谱遥感影像的蓝波段的同时,能够消除大气效应对影像的影响;同时,基于模拟出的蓝波段可以合成高保真的自然色影像。     

CBERS与TM遥感影像湿地覆被分类对比分析

为了对比CBERS与TM两种遥感影像在地表覆被信息提取中的具体性能,验证基于CBERS遥感影像进行湿地覆被分类的可行性,以典型内陆淡水湿地区为对象,基于CBERS与TM遥感影像,针对各波段进行信息量统计及光谱特性分析,获取了各波段覆被探测性能的初步认识;运用非监督、监督与面向对象三种代表性分类方法进行分类实验,通过精度误差矩阵对比分类结果,分析了两种遥感影像在湿地覆被分类中的准确程度差异;基于分类结果,通过景观格局指数计算,对比分析了两种影像在湿地覆被信息提取结果上的空间差异和特性。     

结合曲率滤波的HTM算法去除遥感影像云雾

目的 遥感卫星幅宽较大，成像区域内的薄云和雾很难区分，云雾降低了遥感影像的解译精度和对目标地物判别的准确性。传统的云雾去除方法是通过调整图像的对比度和饱和度来提高重建图像的质量，对不均匀分布云雾的适应性不强。为此，本文以"高分二号"（GF-2）遥感数据为例，提出一种结合高斯曲率滤波的雾度图（haze thickness map，HTM）求解算法。方法 采用遥感影像的红波段进行HTM求解，首先通过不重叠的滑动窗口对整幅图像取暗像素，得到HTM估计值，利用高斯曲率滤波对其进行平滑，减少噪声干扰，保持地物边缘特征，并通过插值运算恢复到原图尺寸；然后利用改进的2维最大熵自动确定分割阈值，提取HTM中白色区域并抑制，对边缘处的像素值进行校正；最后通过HTM结果恢复出清晰影像。结果 由目视判读结合评价指标进行评价，将改进的暗原色先验法、传统的HTM算法与本文改进的方法在不同地区含云雾的遥感影像上进行对比实验。本文改进方法所得结果与传统方法相比，灰度均值降低约34.96%，平均梯度提升约18.48%，信噪比提升约34.77%，对比度提升约39.41%，对于不均匀遮挡的云雾去除具有较好效果。结论 改进的方法能够去除云雾干扰，有效改善影像数据的视觉效果，同时能够保留大量的细节信息，较传统方法更优。     

改进暗通道先验的高分二号影像快速去薄云

为了有效消除薄云对高分二号影像的干扰,提出了一种基于改进暗通道先验的遥感影像快速去薄云方法。首先设置最大大气光阈值,解决原始暗通道先验法对高亮区域的过校问题;然后使用下采样和插值算法简化透射率图的输入,大幅提高处理速度;最后针对成果图像的偏蓝现象,利用散射模型以蓝光波段推算其余波段的改正量。实验结果表明,该方法能有效去除高分二号影像中的薄云,且在纹理色彩、校正真实性和处理效率上都有较好的表现,具有较高的实用价值。     

混合型强迫不变的遥感影像薄云、雾去除方法

针对影像判读中云雾干扰去除难的问题,基于大气散射理论,提出一种"HSI颜色空间变换+强迫不变"混合型强迫不变遥感影像薄云、雾去除方法。首先利用Landsat-8卷云波段提取云雾因子;然后进行HSI颜色空间变换,计算出I分量与云雾因子的相关曲线;通过曲线拟合、滤波以及平滑,最终平化为某一值,使得I分量光谱值不随云雾因子值变化,与云雾因子无相关性,即强迫不变;最后依据像元分解理论分离出像素中的薄云、雾光谱贡献量,达到去除云雾目的。实验表明,该方法无须传输参数,处理方便,处理后影像地物轮廓显现,清晰度提高,效果较好。     

森林叶面积指数遥感反演模型构建及区域估算

基于eCognition面向对象分类算法及校正后的TM遥感影像,获取研究区2010年土地利用/覆被数据。同时在ArcGIS平台下,提取遥感影像6个波段反射率及RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI和WI等10个植被指数,并辅助于DEM、ASPECT、SLOPE等地形信息,在与植物冠层分析仪(TRAC)实测各森林类型叶面积指数相关性分析的基础上,研究表明:相对多元线性回归方法,偏最小二乘法能够更好地把握各森林类型LAI动态变化,而后结合研究区森林覆被信息进行区域估算。     

一种基于物理机理的高光谱遥感图像真彩色校正模型

针对高光谱遥感影像由于各波段光谱范围窄,难以获得符合人们视觉效果的真彩色合成影像问题,提出一种基于物理机理的高光谱遥感图像真彩色校正模型。该模型充分利用高光谱影像在红、绿、蓝反射区的所有谱段信息,通过插补波段并进行波段加权积分重建真彩色合成图像,进而结合实测地物反射率光谱,利用辐射传输模拟的方式,构建具备普适性的真彩色校正模型。利用航空高光谱遥感影像进行色彩校正实验的结果表明,所构建的真彩色校正模型能够很好地应用于高光谱遥感影像真彩色校正。     

一种改进的卫星影像地形校正算法

地形校正的目的主要是补偿由于不规则的地形起伏而造成的地物亮度的变化。由于这种变化会导致相似或同种植被的反射率不一致而影响遥感影像的分类精度,因此,精确的地形校正不仅能提高影像分类的精度,而且还是遥感应用的前提。C校正方法是常用的一种基于影像像素值和太阳入射角余弦值之间线性关系的经验校正方法,但它必须对遥感影像的每一波段都进行像素值和入射角余弦值的线性拟合,这是一个复杂费时的过程。为了改善校正效果和节省校正时间,提出了一种改进的C校正算法,即在不进行线性拟合的情况下也可以达到良好的校正效果。利用三峡地区的TM影像和DEM数据所做的实验证明,该改进方法对影像进行地形辐射校正的结果比原始的C校正算法以及余弦校正都有较大的提高,且优于C校正模型。     

基于透射率拉伸的光学遥感影像薄云去除

针对目前光学遥感影像薄云去除算法中易出现地物失真的问题,基于遥感影像像素至少在某一波段的像素值近似为零这个暗通道先验,提出了一种利用伽马函数非线性拉伸透射率进而做到高保真去薄云的算法。通过透射率的非线性拉伸,增强了无云区域和有云区域的透射率对比度,从而对受薄云影响最严重的蓝光波段进一步去云。定量分析与实验结果表明,该方法不仅能有效去除遥感影像中的薄云,还能保留无云区域的辐射信息,具有高保真特性。     

基于透射率拉伸的光学遥感影像薄云去除

针对目前光学遥感影像薄云去除算法中易出现地物失真的问题,基于遥感影像像素至少在某一波段的像素值近似为零这个暗通道先验,提出了一种利用伽马函数非线性拉伸透射率进而做到高保真去薄云的算法。通过透射率的非线性拉伸,增强了无云区域和有云区域的透射率对比度,从而对受薄云影响最严重的蓝光波段进一步去云。定量分析与实验结果表明,该方法不仅能有效去除遥感影像中的薄云,还能保留无云区域的辐射信息,具有高保真特性。     

Automated masking of cloud and cloud shadow for forest change analysis using Landsat images

Accurate masking of cloud and cloud shadow is a prerequisite for reliable mapping of land surface attributes. Cloud contamination is particularly a problem for land cover change analysis, because unflagged clouds may be mapped as false changes, and the level of such false changes can be comparable to or many times more than that of actual changes, even for images with small percentages of cloud cover. Here we develop an algorithm for automatically flagging clouds and their shadows in Landsat images. This algorithm uses clear view forest pixels as a reference to define cloud boundaries for separating cloud from clear view surfaces in a spectral-temperature space. Shadow locations are predicted according to cloud height estimates and sun illumination geometry, and actual shadow pixels are identified by searching the darkest pixels surrounding the predicted shadow locations. This algorithm produced omission errors of around 1% for the cloud class, although the errors were higher for an image that had very low cloud cover and one acquired in a semiarid environment. While higher values were reported for other error measures, most of the errors were found around the edges of detected clouds and shadows, and many were due to difficulties in flagging thin clouds and the shadow cast by them, both by the developed algorithm and by the image analyst in deriving the reference data. We concluded that this algorithm is especially suitable for forest change analysis, because the commission and omission errors of the derived masks are not likely to significantly bias change analysis results.     

极轨气象卫星图像的云自动检测方法研究

图像上云的存在给遥感图像的处理与分析带来了不便。本文探讨了利用极轨气象卫星图像可见光波段的反射率和热红外波段的亮温信息进行云自动检测的方法。该方法在可见光波段是否有效判断的基础上,进行图像水陆区域划分,然后对不同图像、不同波段的水域和陆域分别设定合理的阈值,实现了云自动检测目的。试验结果表明,该方法能够取得较高的云检测精度。     

Cloud detection for high-resolution remote-sensing images of urban areas using colour and edge features based on dual-colour models

ABSTRACT

In this article, the colour and edge features of clouds are proposed based on the analysis of a large number of cloudy high-resolution remote-sensing images of urban area. Using the cloud features, a cloud detection method based on dual-colour space is proposed. First, two candidate cloud regions maps are built based on hue–saturation–intensity model and blue, green, red model, respectively. According to the superposition analysis of the two maps, fine cloud regions map are obtained. After removing the fine cloud regions from the image, the image is linearly stretched to enhance the intensity of thin clouds around the fine cloud regions. In the stretched image, the thin cloud regions map is built with the threshold technique. Finally, the fine cloud regions map and the thin cloud regions map are combined to form the complete cloud regions map. The results of experiments and comparisons indicate that our algorithm has good accuracy and efficiency.     

Energy-based cloud detection in multispectral images based on the SVM technique

In this paper, the energy characteristics of Gabor texture are used for cloud detection in high-resolution multispectral images. First, the satellite remote-sensing image is divided into superpixels using simple linear iterative clustering (SLIC), and then, the energy characteristics of Gabor texture and spectral characteristics are computed by extracting the texture features of the superpixels. The features of the cloud superpixels are used as the learning sample of the support vector machine (SVM) classifier, and a classification model is obtained by training the SVM classifier. Finally, a cloud-detection experiment is conducted for various sensor images with three visible bands and one near-infrared band. The experimental results showed that the proposed method provides an excellent average overall accuracy for thick and thin clouds in a complex background of forests, harbours, snow and mountains. The characteristic parameters of this paper are not limited by the image parameters; thus, they provide good results and universality for various types of sensors.     

Cloud motion in the GOCI/COMS ocean colour data

The Geostationary Ocean Colour Imager (GOCI) instrument, on Korea’s Communications, Oceans, and Meteorological Satellite (COMS), can produce a spectral artefact arising from the motion of clouds – the cloud is spatially shifted and the amount of shift varies by spectral band. The length of time it takes to acquire all eight GOCI bands for a given slot (portion of a scene) is sufficient to require that cloud motion be taken into account to fully mask or correct the effects of clouds in all bands. Inter-band correlations can be used to measure the amount of cloud shift, which can then be used to adjust the cloud mask so that the union of all shifted masks can act as a mask for all bands. This approach reduces the amount of masking required versus a simple expansion of the mask in all directions away from clouds. Cloud motion can also affect regions with unidentified clouds – thin or fractional clouds that evade the cloud identification process – yielding degraded quality in retrieved ocean colour parameters. Areas with moving and unidentified clouds require more elaborate masking algorithms to remove these degraded retrievals. Correction for the effects of moving fractional clouds may also be possible. The cloud shift information can be used to determine cloud motion and thus wind at the cloud levels on sub-minute timescales. The beneficial and negative effects of moving clouds should be considered for any ocean colour instrument design and associated data processing plans.     

单幅光学遥感影像去霾算法及评价综述

光学遥感影像经常受到云或霾影响,而在多数情况下极少能获取辅助数据用于遥感影像去霾;因此单幅光学遥感影像的图像处理去霾算法成为遥感影像预处理的重要技术。目前,不同研究者设计了多种算法,但是缺乏系统性的总结与对比分析,本文旨在系统性地总结单幅遥感影像去霾算法的研究进展,并提供典型算法的基本原理、优缺点及适用场景。采用文献综合分析方法从霾条件影像成像模型、基础原理和结果评价3方面对当前的去霾算法进行归类总结和原理剖析,然后结合具体应用场景分析算法的适用范围和存在问题,并提出可行的解决方案。常见的去霾算法大体可归纳为暗目标减法、滤波法、暗通道先验法和经验变换法4类,这些算法采用的霾条件影像成像模型包括加法模型、霾传输衰减模型和照明—反射模型等;在算法的评估中,常用的手段有主观分析方法、影像光谱特征分析方法以及图像质量指标评估法等。现有算法并不能适用于所有的场景或图像,存在模型参数难以自适应调整、模型对特殊地物类型敏感、处理结果失真严重等问题;算法的评价主要采用主观对比分析方法,根据应用需求构建客观指标成为目前的热点方向。     

Cloud detection from thermal infrared images using a segmentation technique

In this paper, a fast and accurate method is proposed for two cloud detection tests on thermal infrared (IR) data obtained from satellite images over sea; an IR gross cloud test and a spatial coherence test. The proposed method is based on a regional segmentation technique. After the segmentation of an IR image, small regions were regarded as cloudy due to their high spatial variability in temperature. This technique preserved the spatial resolution of the detected cloud image which would be degraded by the conventional spatial coherence test. It also reduced the computation dramatically, compared to the conventional spatial coherence test. An accurate temperature threshold between clear sea and clouds was determined directly from the segmented image. This post-determined threshold was found to be more accurate than pre-determined temperature thresholds. Since this algorithm does not require any human interaction, it can be combined with other tests in an automatic cloud detection algorithm.     

基于云影距离模型的遥感图像多尺度融合算法

为了对含云及云影遥感图像上的云影进行有效消除,首先构建了遥感云影成像距离模型,并依据遥感云影距离模型,提出了一种新的云影识别算法,用来对有云遥感图像中的云层和阴影进行检测与识别,并生成融合区域图;然后在融合区域图的基础上,提出了一种多尺度图像融合算法,用来消除云影,即先对源图像进行多尺度几何分解,得到源图像的粗糙和细节分量,然后对云影区域和非云影区域分别采用不同的融合策略进行处理得到融合图像。仿真实验结果表明,该新融合算法不仅对薄云的消除效果非常好,而且对厚云及阴影的消除效果也非常好,且不会产生衔接边缘。     

先验终端像元库支持下的GF-4多光谱影像自动云检测

高分四号卫星是我国第一颗地球同步轨道遥感卫星,以其高频、宽幅的特点,可为我国农业、林业、减灾、气象、环保和水利等应用提供快速、稳定的光学遥感影像,高效的影像自动云检测有助于进一步提高高分四号影像的利用效率。CDAG（Cloud Detection Algorithm-Generating）是一种基于像元组分光谱分析的自动云检测算法,能有效降低混合像元、复杂表面结构和大气等因素的影响。为了探索CDAG算法对于高分4号多光谱影像（GF4-PMS）的云检测应用能力,首先,从高光谱影像（AVIRIS）上选取不同的云类型和各种地表覆盖类型,建立云像元库和地物像元库;其次,基于高光谱像元库和GF4-PMS传感器光谱响应函数模拟出多光谱影像像元库;然后,根据碎云、薄云、厚云及非云像元的光谱差异性分析,将GF4-PMS影像的待检测像元与终端像元进行相似概率分析,实现基于最佳阈值自动迭代的GF4-PMS影像云检测;最后,从云像元正确率、晴空像元正确率、误判率、漏判率等多个指标进行云检测精度验证。结果表明：AVIRIS影像可以有效提取适用于GF4-PMS影像云检测的终端像元库,基于CDAG算法能较好地识别GF4-PMS影像上各种类型的云,对于不同时相、不同下垫面的碎云、薄云、厚云的检测精度可达90%以上。因此,基于先验终端像元库的云检测法对于提升GF4-PMS影像的利用效率具有较好的应用价值。     

Automatic Cloud Detection of GF4 Multispectral Imagery Supported by the Priori Terminal Pixel Library

The GaoFen4 (GF4) satellite is China’s first geo-synchronous orbit remote sensing satellite. With the advantages of high frequency and wide width, it can provide fast and stable optical remote sensing images for agricultural, forestry, disaster reduction, meteorology, environmental protection, water conservancy and other applications in China. Efficient image automatic cloud detection helps to further improve the utilization efficiency of GaoFen4 images. CDAG（Cloud Detection Algorihtm-Generating）Cloud detection is an automatic cloud detection algorithm based on spectral analysis of pixel components, which can effectively reduce the influence of mixed pixels, complex surface structure and atmosphere. This paper aims to explore the application of CDAG algorithm in cloud detection of GaoFen4 multispectral imagery (GF4-PMS). Firstly, different cloud types and surface cover types were selected from hyperspectral images (AVIRIS) to establish cloud pixel library and clear sky pixel library. Next, the pixel library of multispectral images was simulated based on Hyperspectral pixel library and spectral response function of GF4-PMS sensor. Then, according to the spectral difference analysis of broken cloud, thin cloud, thick cloud and non-cloud pixel, the similarity probability analysis was performed on the to-be-detected pixel of the GF4-PMS image and the terminal pixel, and the GF4-PMS image cloud detection based on the optimal threshold automatic iteration was realized. Finally, cloud detection accuracy verification was carried out from multiple indicators such as cloud pixel correct rate, clear sky pixel correct rate, false positive rate and missed rate. The results show that AVIRIS images can effectively extract terminal pixel libraries for GF4-PMS image cloud detection. Clouds of Various types on GF4-PMS images can be better identified based on the CDAG algorithm. The accuracy of detection results for broken clouds, thin clouds and thick clouds with different time phases and different underlying surfaces can reach more than 90%. Therefore, the cloud detection method based on the priori terminal pixel library has a good application value for improving the utilization efficiency of GF4-PMS images.