共查询到20条相似文献，搜索用时 171 毫秒
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研究引潮力的相位变化周期与发震的关系,进而确定地震大气温度增强异常识别的背景指示时间,采用大气分层技术,处理美国国家环境中心的NCEP大气温度数据,分析了昆仑山MS8.1地震前后不同高度层的大气温度垂直分布动态演化,结果显示:地震发生时引潮力值所处最大振幅相位附近,反映引潮力对本次地震的发生具有触、诱发的作用;孕震区地表及其上附多层大气热变化经历震前起始增温,震后消亡的连续时间演变过程,增温区集中在地震活动断裂带及其附近区域,呈现出与构造紧密关联的非均匀加热,与岩石受力,由形变—破裂过程中向外热辐射变化过程相吻合,表明大气增温与昆仑山地震活动相关;热增强表现出自下而上的从地表开始增温,并随大气运动抬升扩散,在一定高度的高空逐渐消亡的过程,符合地面对大气加热导致大气升温、抬升、扩散、消亡的大气热动力学特性,表明下垫面构造运动是本次温度异常变化的主控原因;大气增温过程与引潮力(低值—高值)的变化过程具有一定的同步性,显示引潮力为地震大气温度异常识别过程中,背景温度选择提供具有力学含义、可预先计算获得的时间指示,而通过引潮力周期获得的大气温度变化反映了临震构造应力的变化,将引潮力变化与大气温度垂直分层分析结合,将有助于区分地震热异常与非震热异常。 相似文献
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大气活动的干扰是遥感技术发展的最大限制之一,对星载的可见光、近红外波段的光学探测器而言,大气中云雾的干扰更是不易处理。为了促进CBERS-02星CCD传感器图像的定量化应用,在对以往有关遥感图像薄云去处方法的研究的基础上,本文提出了对含薄云的CBERS-02 CCD图像进行适当层次的小波分解,然后对分解的图像进行同态滤波,最后进行图像重构而去除薄云的方法。试验结果表明,该方法既能有效的去除薄云,又可较好的保持图像细节。 相似文献
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为了去除厚云及其阴影对光学遥感图像的影响,以TM图像为实验数据提出一种基于光谱及几何信息的去厚云算法.在分析单图像云区多光谱特征及对比多时相图像光谱特征的基础上,首先检测光谱特征明显的厚云,依据云和云影成对出现的成像几何关系,按确定的方位和距离搜索云影;然后采用数学形态学的侵蚀与膨胀算法对云区边缘进行碎片去除及填补、合并处理,使其准确反映图像中受云影响的数据总量;最后利用光谱线性回归匹配后的参考图像替换目标图像中的云区,达到去云目的.实验结果表明,上述算法去厚云效果显著,能够有效排除水体及地形阴影对云影识别的影响,具有快速、简单、实用性强的特点. 相似文献
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利用2015年皮山6.5级地震前半年的MODIS影像,通过劈窗算法对震中周边地表温度进行反演,通过对比地表反演温度和震中周边气象台站气象记录,研究发现震中附近在震前80 d和15 d左右出现明显的热异常,但在震前1个月左右存在短暂的温度低异常现象。另外,根据近22年的气象记录,震中及周边在震前半年气温明显偏高,这种气象异常或可视为地震前兆。 相似文献
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郭保 《测绘与空间地理信息》2021,44(10):150-152
在光学遥感卫星图像中,云是普遍存在的现象,它严重降低了图像的质量,因此,去云处理就是一个必不可少的步骤.深度神经网络在许多图像处理任务中取得了成功,但是利用该方法针对遥感图像的去云研究较少.本文采用GAN来解决遥感图像去云问题,首先训练生成模型生成无云影像,同时训练判别模型使生成的模型更加真实和清晰,最终达到从被云覆盖的卫星图像中恢复并增强这些区域的信息,生成质量更好的无云图像的目的.基于人工智能标注的Sentinel-2卫星遥感影像数据集的试验表明,与传统的小波变换基准相比,提出的生成对抗网络模型在去云处理方面效果有明显提升. 相似文献
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针对云检测在高亮度地表以及雪覆盖区域存在过度检测的问题,设计了一种不依赖热红外波段的增强型多时相云检测EMTCD(Enhanced Multiple Temporal Cloud Detection)算法。首先,利用云的光谱特征建立单时相云检测规则,并基于云、雪的光谱差异构建了增强型云指数ECI(Enhanced Cloud Index),改进了云、雪的区分能力;其次,以同一区域无云影像为参考,基于ECI指数构建了多时相云检测算法,较好地克服了单时相云检测中高亮度地表、雪和云容易混淆的问题,提高了云检测的精度;最后,选择两个典型区域的Landsat-8 OLI影像,对比分析了不同算法的云检测结果。实验结果表明:ECI指数能够有效区分云、雪,EMTCD方法的平均检测精度达到93.2%,高于Fmask(Function of mask)(81.85%)、MTCD(Multi-Temporal Cloud Detection)(66.14%)和Landsat-8地表反射率产品LaSRC(Landsat-8 Surface Reflectance Code)的云检测结果(86.3%)。因此,本文提出的EMTCD云检测算法能够有效地减少高亮度地表和雪的干扰,实现不依赖热红外波段的高精度云检测。 相似文献
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不同时相遥感图像的镶嵌技术 总被引：10，自引：0，他引：10
本文介绍针对不同时相遥感图像镶嵌中的技术难题开发的一整套数字镶嵌方法。采用递推式灰度调整、滑动窗口探测最佳拼接点及对其两侧作加权灰度圆滑方法, 以消除被镶嵌图像之间因色调差异造成的接缝；采用在相邻图像重叠区内选取控制点并按控制点追踪进行镶嵌方法, 以提高镶嵌的几何精度；还采取了单元镶嵌法, 以解决遥感图像数据量庞大而计算机容量有限的矛盾。用上述技术和作者开发的相应软件, 成功地制作了长江中下游等多个地区的TM影像镶嵌图, 受到应用人员好评。 相似文献
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One of the main problems of optical remote sensing is clouds and cloud shadows caused by specific atmospheric conditions during data acquisition. These features limit the usage of acquired images and increase the difficulty in data analysis, such as normalized difference vegetation index values, misclassification, and atmospheric correction. Accurate detection and reliable cloning of cloud and cloud shadow features in satellite images are very useful processes for optical remote sensing applications. In this study, an automated cloud removal algorithm to generate cloud and cloud shadow free images from multitemporal Landsat-8 images is introduced. Cloud and cloud shadow areas are classified by using process-based rule set developed by using spectral and spatial features after applying simple linear iterative clustering superpixel segmentation algorithm to the image to find cloud pixel groups easily and correctly. Segmentation-based cloud detection method gives better results than pixel-based for detection of cloud and cloud shadow patches. After detection of clouds and cloud shadows, cloud-free images are created by cloning cloudless regions from multitemporal dataset. Spectral and structural consistency are preserved by considering spectral features and seasonal effects while cloning process. Statistical similarity tests are applied to find best cloud-free image to use for cloning process. Cloning results are tested with the structural similarity index metric to evaluate the performance of cloning algorithm. 相似文献
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Automatic change detection and geo-database updating in the urban environment are difficult tasks. There has been much research on detecting changes with satellite and aerial images, but studies have rarely been performed at the street level, which is complex in its 3D geometry. Contemporary geo-databases include 3D street-level objects, which demand frequent data updating. Terrestrial images provides rich texture information for change detection, but the change detection with terrestrial images from different epochs sometimes faces problems with illumination changes, perspective distortions and unreliable 3D geometry caused by the lack of performance of automatic image matchers, while mobile laser scanning (MLS) data acquired from different epochs provides accurate 3D geometry for change detection, but is very expensive for periodical acquisition. This paper proposes a new method for change detection at street level by using combination of MLS point clouds and terrestrial images: the accurate but expensive MLS data acquired from an early epoch serves as the reference, and terrestrial images or photogrammetric images captured from an image-based mobile mapping system (MMS) at a later epoch are used to detect the geometrical changes between different epochs. The method will automatically mark the possible changes in each view, which provides a cost-efficient method for frequent data updating. The methodology is divided into several steps. In the first step, the point clouds are recorded by the MLS system and processed, with data cleaned and classified by semi-automatic means. In the second step, terrestrial images or mobile mapping images at a later epoch are taken and registered to the point cloud, and then point clouds are projected on each image by a weighted window based z-buffering method for view dependent 2D triangulation. In the next step, stereo pairs of the terrestrial images are rectified and re-projected between each other to check the geometrical consistency between point clouds and stereo images. Finally, an over-segmentation based graph cut optimization is carried out, taking into account the color, depth and class information to compute the changed area in the image space. The proposed method is invariant to light changes, robust to small co-registration errors between images and point clouds, and can be applied straightforwardly to 3D polyhedral models. This method can be used for 3D street data updating, city infrastructure management and damage monitoring in complex urban scenes. 相似文献
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针对在光学遥感图像中的地球表面反射率由于云覆盖所造成的衰减问题,本文提出了一种基于噪声调整主成分变换（NAPCT）模型的薄云去除方法。首先,通过噪声估计构建NAPCT除云模型,实现云图像的转换;然后,利用NAPCT第一成分（NAPC1）提供的云分布信息,通过合并云掩膜,对NAPC1上的浑浊像素进行校正;最后,将逆变换应用于云覆盖区域,并与原始的清晰像素镶嵌在一起,最终得到无云图像。利用模拟和真实的Landsat 8图像对本文方法的性能进行了定性和定量评估,试验结果表明：与传统方法相比,本文NAPCT方法提供了更好的均方根误差和峰值信号噪声比,在除云上具有更好的效果。 相似文献
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本文详细论述了大区域遥感图像的数字镶嵌设计思想,提出了分区加大重叠的镶嵌方法,并 讨论了色调与反差匹配、几何配准和选择最佳匹配区域等问题。为了完善计算机图像镶嵌技术, 本文还提出了消除镶嵌接缝的加权平均调整灰度的方法。 相似文献