共查询到20条相似文献，搜索用时 156 毫秒
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光学遥感影像经常受到云雾的干扰而导致数据质量下降。在暗原色先验理论和云雾影像模型的基础上,提出了一种顾及空间和光谱差异的单幅遥感影像自适应云雾去除方法,较好地解决了传统暗原色先验在高亮地物校正过度和部分波段云雾校正不足的问题,有效地实现了影像云雾去除。首先通过高亮地物的光谱特征构建高亮地物判别指数,辅以密度分割对其进行提取分类,在此基础上引入自适应校正函数对该区域透射率进行修正;其次在对多幅云雾遥感影像实验分析的基础上,提出了符合大气散射机制的波段透射率校正系数,实现了波段间处理强度的自适应调整。实验结果表明,该方法能有效去除不同波段的云雾干扰,并避免高亮地物的过度校正,可取得较好的复原结果。 相似文献
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针对云雾天获取的影像对比度低、地物细节模糊,严重影响影像后期处理的问题,该文基于WorldView-3(WV-3)遥感影像,通过薄云最优化变换(HOT)与点云算法相结合、暗通道两种方法去除或削减图像中云层信息,并采用相关系数、光谱扭曲度等参数定量评价两种方法的去云效果.结果显示,HOT与点云算法结合与暗通道两种方法均能对WV-3影像中云层信息进行去除,其中后者去云结果各波段的相关系数比前者去云结果平均高出6.9％,后者的光谱扭曲度比前者平均低出1.67％.因此暗通道去云方法要优于HOT与点云算法结合的去云方法. 相似文献
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《地理空间信息》2020,(8)
对可见光波段遥感图像进行HSV色彩空间变换,结合图像的亮度特征和饱和度特征,对饱和度通道和亮度通道分别进行同态滤波和直方图均衡化处理,增强目标云区对比度;转换为RGB色彩空间之后,接着进行2%的线性拉伸,结合云的暗原色通道特性生成暗通道图,不断突出云区与非云区对比;最后进行otsu阈值分割提取云区。实验依托实际项目需求,针对原始影像波段少的特点,选取3幅压缩后全色遥感影像为实验数据,并与阈值法进行目视判别和精度统计对比。实验结果表明该方法可以快速、较高精度地检测出云区,所提出的方法对检测数据质量和波段数要求低,检测快速,检测云区较为完整准确、实用性强。 相似文献
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针对含雾遥感影像在军事航空侦查、地物判读等方面使用率低、有效性差的问题,以及现有去雾算法中存在计算复杂耗时、色彩失真的弊端,结合遥感影像景深变化小、不含天空背景等特点,本文提出一种改进的暗原色先验去雾算法。首先,对影像中白色场景灰度值进行统计并设定阈值划分为失效区,分离水域与非水域减少蓝色波段在水域的占比,合成新的蓝色波段,以改进暗通道值的获取方法;其次,采用导向滤波替代软抠图法优化透射率提升处理时间;然后,对关键参数进行适应性改进试验并采用自动色阶恢复去雾后的影像色彩;最后,利用含雾的无人机影像和GF-2影像进行了试验,并进行了定量评价。试验结果表明,在同等试验条件下,本文方法处理单幅影像的时间比暗原色先验去雾算法的提升4倍以上,且去雾后影像的灰度均值、标准差、信息熵、平均梯度等指标比暗原色先验去雾算法得到的值均有提高,能有效提高有雾影像的清晰度,增强影像色彩和细节。 相似文献
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针对传统算法在单幅影像色彩平衡处理过程中色彩失真、亮度恢复不佳等问题,以同类地物要素的亮度信息和标准亮度值,基于HIS色彩空间构建环境因子和时间因子造成的影像亮度多项式补偿函数,以此修正可见光影像的亮度信息。通过实验表明：相较于Mask算法,本文算法模型可以有效修复单幅影像的亮度信息,且信息表达更加丰富;相较于Retinex算法,本文算法模型在修复影像亮度值的同时可以更好地保留地物要素的真实色彩信息,但当影像3个波段亮度值相同时,本文算法会出现红斑效应。 相似文献
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一种城市高大地物阴影检测方法 总被引：2，自引：0，他引：2
针对水体、偏蓝色地物会影响阴影检测精度这一问题,该文提出了一种基于WorldView-2影像的城市高大地物阴影检测方法。首先,在统计分析阴影及典型地物光谱值的基础上,对C1C2C3色彩不变空间中的C3分量利用直方图阈值法分割阴影区域和非阴影区域,分离后的阴影区域中包含了绿色植被信息;然后,利用多波段运算提取植被信息;其次,对最初的阴影植被混合区域和植被区域做逻辑差运算,得到目标阴影区域;最后,对阴影提取结果进行小区域去除和形态学滤波等后处理。实验结果表明,该方法针对WorldView-2影像具有普适性,能够有效避免水体、偏蓝色地物的影响,能够精确、快速地检测阴影区域。 相似文献
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《国土资源遥感》2017,(3)
结合高光谱影像地物光谱特征与高空间分辨率影像分割获得的目标对象进行地物分类。首先,对Hyperion影像进行坏线和Smile效应去除,经过FLAASH大气校正后,得到研究所用的155个波段;其次,利用地物光谱曲线的特征点确定适合地物识别的光谱分辨率,进行Hyperion影像降维,生成降维后所需的21个宽波段;然后,对IKONOS影像采用小波融合,利用多分辨率分割技术生成高空间分辨率影像目标对象;最后,基于层次分析法对分割后生成的目标对象进行分类,采用模糊隶属函数利用植被红边效应、水体在近红外波段吸收特征进行第1层次分类,再取距离值最大的前10个Hyperion影像波段作为标准最邻近分类的特征波段,完成第2层次分类。分类结果表明,研究区共分出9种地物类型,分类效果明显优于最大似然法分类与光谱角填图法。 相似文献
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高光谱遥感影像具有光谱分辨率极高的特点,承载了大量可区分不同类型地物的诊断性光谱信息以及区分亚类相似地物之间细微差别的光谱信息,在目标探测领域具有独特的优势。与此同时,高光谱遥感影像也带来了数据维数高、邻近波段之间存在大量冗余信息的问题,高维度的数据结构往往使得高光谱影像异常目标类和背景类之间的可分性降低。为了缓解上述问题,本文提出了一种基于波段选择的协同表达高光谱异常探测算法。首先,使用最优聚类框架对高光谱波段进行选择,获得一组波段子集来表示原有的全部波段,使得高光谱影像异常目标类与背景类之间的可分性增强。然后使用协同表达对影像上的像元进行重建,由于异常目标类和背景类之间的可分性增强,对异常目标像元进行协同表达时将会得到更大的残差,异常目标像元的输出值增大,可以更好地实现异常目标和背景类的分离。本文使用了3组高光谱影像数据进行异常目标探测实验,实验结果表明,该方法与其他现有高光谱异常目标探测算法对比,曲线下面积AUC(Area Under Curve)值更高,可以更好地实现异常目标与背景分离,能够更有效地对高光谱影像进行异常目标探测。 相似文献
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Pixel-based or texture-based classification technique individually does not yield an appropriate result in classifying the high spatial resolution remote sensing imagery since it comprises textured and non-textured regions. In this study, Hölder exponents (HE) and variance (VAR) are used together to transform the image for measuring texture. A threshold is derived to segment the transformed image into textured and non-textured regions. Subsequently, the original image is extracted into textured and non-textured regions using this segmented image mask. Afterward, extracted textured region is classified using ISODATA classification algorithm considering HE, VAR, and intensity values of individual pixel of textured region. And extracted non-textured region of the image is classified using ISODATA classification algorithm. In case of non-textured region, HE and VAR value of individual pixel is not considered for classification for significant textural variation is not found among different classes. Consequently, the classified outputs of non-textured and textured regions that are generated independently are merged together to get the final classified image. IKONOS 1 m PAN images are classified using the proposed algorithm, and the classification accuracy is more than 88%. 相似文献
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遥感影像敏感目标隐藏是保证遥感资源安全共享的关键。针对传统方法存在的目标检测不完全、补全结果不可靠的问题,提出了一种基于Transformer结构的遥感影像敏感目标自动隐藏方法。首先利用以Swin Transformer为主干网络的Cascade Mask R-CNN（region-based convolutional neural network）实例分割优化模型检测敏感目标并生成掩膜区域,同时设计了RSMosaic（remote sense Mosaic）合成数据方法减少人工标注数据;然后,基于色相-饱和度-明度（hue-saturation-value, HSV）空间的阴影检测模型扩展掩膜区域;最后,引入MAE（masked autoencoders）模型实现目标背景生成。以飞机目标为例,与Partial-Connvolutios和EdgeConnec进行了对比实验。结果表明,相比传统方法,该方法在敏感目标实例分割中的边界框与像素掩膜AP值分别提升了13.2%与11.2%;在使用RSMosaic合成数据后,边界框与像素掩膜AP值可分别再提升9.39%与14.16%, 且图像修补中的平均绝对误差和最大平均差异提升80%以上,实现了结构合理、纹理清晰的敏感目标自动隐藏效果。 相似文献
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In the studies reteted to surface energy balance, satellite data provides important inputs for estimating regional surface albedo and evapotranspiration. The paper describes the use of satellite data in determining the surface emissivity over heterogeneous a’reas by taking Normalized Difference Vegetation Index (NDVI) as modulating parameter at pixel resolution. The estimated emissivity values have been used to find the surface temperature at the pixel scale. Landsat-TM-visible, NIR, TIR bands data and some ground meteorological data have been used in an energy balance model for estimating surface albedo and evapotranspiration. The ET values derived from the model are in good agreement with the values obtained with. ‘CENTURY MODEL’ and ground observations over the area, suggesting the possible use of this approach fot regional scale studies on evapotranspiration. 相似文献
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目前,遥感影像应用的场景越来越多,但受到空气污染或气候等原因影响,局部地区雾霾现象严重,严重影响遥感影像质量。针对这一问题,本文结合遥感影像的特点提出了一种基于暗原色先验的遥感影像去雾方法。首先,结合遥感影像的特点,在求取暗通道过程中设置失效点,排除不符合暗原色先验的像素点;其次,根据获得的暗通道计算大气光值;然后,求取透射率图并用导向滤波精细化,根据大气传输模型求得去雾后的影像;最后,分别用无人机影像和Landsat卫星遥感影像进行去雾实验,验证了本文方法的有效性。 相似文献
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Many sensors have their bands overlapped and therefore do not set a normal space. If a spectral distance is measured, as in first-order statistical classifiers, the direct consequence is that the result will not be the most accurate. Image classification processes are independent of the spectral response function of the sensor, so this overlap is usually ignored during image processing. This paper presents a methodology that introduces the spectral response function of sensors into the classification process to increase its accuracy. This process takes place in two steps: first, incident energy values of the sensors are reconstructed; second, the energy of the bands is set in an orthonormal space using a matrix singular value decomposition. Sensors with and without overlapping spectral bands were simulated to evaluate the reconstruction of energy values. The whole process was implemented on three types of images with medium, high and very high spatial resolution obtained with the sensors ASTER, IKONOS and DMC camera, respectively. These images were classified by ISODATA and minimum distance algorithms. The ISODATA classifier showed well-defined features in the processed images, while the results were less clear in the original images. At the same time, the minimum distance classifier showed that overall accuracy of the processed images increased as the maximum tolerance distance decreased compared to the original images. 相似文献
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Tyas Mutiara Basuki Andrew K. Skidmore Patrick E. van Laake Iris van Duren Yousif A. Hussin 《国际地球制图》2013,28(4):329-345
A main limitation of pixel-based vegetation indices or reflectance values for estimating above-ground biomass is that they do not consider the mixed spectral components on the earth's surface covered by a pixel. In this research, we decomposed mixed reflectance in each pixel before developing models to achieve higher accuracy in above-ground biomass estimation. Spectral mixture analysis was applied to decompose the mixed spectral components of Landsat-7 ETM+ imagery into fractional images. Afterwards, regression models were developed by integrating training data and fraction images. The results showed that the spectral mixture analysis improved the accuracy of biomass estimation of Dipterocarp forests. When applied to the independent validation data set, the model based on the vegetation fraction reduced 5–16% the root mean square error compared to the models using a single band 4 or 5, multiple bands 4, 5, 7 and all non-thermal bands of Landsat ETM+. 相似文献
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Linear spectral mixture analysis (LSMA) is widely employed in impervious surface estimation, especially for estimating impervious surface abundance in medium spatial resolution images. However, it suffers from a difficulty in endmember selection due to within-class spectral variability and the variation in the number and the type of endmember classes contained from pixel to pixel, which may lead to over or under estimation of impervious surface. Stratification is considered as a promising process to address the problem. This paper presents a stratified spectral mixture analysis in spectral domain (Sp_SSMA) for impervious surface mapping. It categorizes the entire data into three groups based on the Combinational Build-up Index (CBI), the intensity component in the color space and the Normalized Difference Vegetation Index (NDVI) values. A suitable endmember model is developed for each group to accommodate the spectral variation from group to group. The unmixing into the associated subset (or full set) of endmembers in each group can make the unmixing adaptive to the types of endmember classes that each pixel actually contains. Results indicate that the Sp_SSMA method achieves a better performance than full-set-endmember SMA and prior-knowledge-based spectral mixture analysis (PKSMA) in terms of R, RMSE and SE. 相似文献
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Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data. 相似文献
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Priyadarshi Upadhyay Anil Kumar S. K. Ghosh 《Journal of the Indian Society of Remote Sensing》2013,41(4):777-786
Fuzzy based soft classification have been used immensely for handling the mixed pixel and hence to extract the single class of interest. The present research attempts to extract the moist deciduous forest from MODIS temporal data using the Possibilistic c-Means (PCM) soft classification approach. Temporal MODIS (7 dates) data were used to identify moist deciduous forest and temporal AWiFS (7 dates) data were used as reference data for testing. The Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI) were used to generate the temporal vegetation indices for both the MODIS and the AWiFS datasets. It was observed from the research that the MODIS temporal NDVI data set1, which contain the minimum number of images and avoids the temporal images corresponding to the highest frequency stages of onset of greenness (OG) and end of senescence (ES) activity of moist deciduous forest have been found most suitable data set for identification of moist deciduous forest with the maximum fuzzy overall accuracy of 96.731 %. 相似文献