一种改进的矩匹配方法在CMODIS数据条带去除中的应用

由于传感器之间对接受的地物辐射信号的响应特性不同,导致CMODIS数据中的许多波段含有大量的条带。这些噪声严重影响了CMODIS数据的解译和信息提取。介绍了几种常用在TM、MSS、SPOT等多传感器光谱仪中条带去除方法,提出了一种改进矩匹配方法用于CMODIS数据中的条带去除,并比较了这种方法和其它几种常用方法对几何纠正前非均匀地物分布的CMODIS数据的去条带噪声结果。结果表明这种新方法要优于以上提到的几种常用方法,具有很好的去条带噪声效果,同时保持图像原有的的信息。这种方法在其它多传感器遥感图像的条带噪声去除中也有很强的适用性。     

遥感分类图像条带噪声的去除

在介绍了几种常用于TM、MSS、SPOT等多传感器遥感图像中条带噪声去除方法的基础上,提出了一种综合利用IDL语言和常用遥感软件(主要为ENVI、ERDAS等)对分类后图像进行条带处理的新方法。并以2005年北京市SPOT图像为试验数据,对该方法进行了尝试。结果表明,利用该方法可弥补一些条带噪声去除方法的弊端,有效地去除分类后图像上的条带噪音;同时避免了分类前期条带去除过程中对条带像元值的不正确计算,以及对图像上正确像元的影响而导致的后期遥感分类过程中的错分误分问题,从而可以有效地提高遥感分类精度。这种方法在其它多传感器遥感图像的条带噪声去除中也有很强的适用性。     

一种高光谱图像条带噪声去除改进算法

传统的矩匹配方法改变了图像在成像行或列方向的均值分布,使原始图像信息发生了较大改变。在分析HJ-1-A星超光谱图像条带噪声的基础上,提出了一种改进的矩匹配方法,将传统矩匹配算法中参考图像的平均值和标准差分别用平滑滤波处理后的列均值和方差来代替。实验结果表明,与传统矩匹配方法相比,该方法能减少图像信息的丢失,并能在保持原始图像特征的前提下有效地去除条带噪声。这种方法在其它多传感器遥感图像的条带噪声去除中也有很强的适用性。     

一种改进的CHRIS/PROBA垂直条带去除方法

多角度高光谱高空间分辨率的CHRIS数据是新一代小卫星PROBA平台上的CHRIS传感器获取的又一卓越的遥感数据源,虽然其数据有诸多优势,但也有一个缺陷:在图像上垂直条带噪声污染严重。本文在San-dra Mannheim提出的去除此类噪声的方法基础上,对该方法进行了另一种途径的探索,基于图像分割的原理,将图像分成灰度相近的几个片段,逐片断应用最小二乘法的多项式拟合,然后将去过条带的片段整合成一幅完整的图像。通过试验证明,用该途径实现的这种方法在地物复杂图像灰度变化剧烈的影像上取得了优越于其他方法的效果,并且证实,对于具有同类条带噪声的OMIS(Airborne Operational Modular Imaging Spectrometer),该方法也取得了比较好的效果,因而值得推广。     

MODIS影像条带噪声行的判断及去除研究

为了提高MODIS_L1B含有条带噪声的波段5和波段27的影像质量,基于MODIS的扫描特性提出了一种通过扫描带行均最大值判断条带噪声行的方法。去除影像的噪声行时,在波段5的单行邻域插值法基础上,针对波段27提出了一种相邻多行插值方法。最后,通过对比原始数据和去条带噪声后数据的差值图、行均值图和数值分析来证实条带噪声处理效果。结果表明:该方法可以精确判断这两个波段影像的所有条带噪声行,且对条带噪声去除效果较好,去除条带过程简单且适用于复杂场景的光谱图像。     

基于小波变换的图像条带噪声去除方法

条带噪声的存在不但妨碍高光谱图像的目视判读,而且制约高光谱遥感的定量应用。针对小波变换法条带噪声去除过程中遇到的条带噪声和图像有用信息难以有效分离的问题,根据小波变换的方向性和数学显微镜特性,提出了一种新的基于小波变换的条带噪声去除方法。这种方法首先对含有条带噪声的图像进行一定层数的小波分解;然后对每一层分解得到的与条带噪声分布方向相同的子图像再进行一定层数的小波分解,从而实现条带噪声和图像有用信息的有效分离,将含有条带噪声的子图像置零;最后利用小波反变换得到去除条带噪声的图像。以欧洲空间局PROBA卫星上搭载的CHRIS高光谱数据为例,采用相关系数(R)、结构相似度(SSIM)和峰值信噪比(PSNR)3个定量指标,对比分析了新方法与矩匹配法、傅立叶滤波法和小波阈值法的条带噪声去除效果。结果表明新方法去噪后的图像具有最高的R、SSIM和PSNR,新方法能够有效地去除高光谱图像中的条带噪声,同时较好地保留了原始图像的有用信息。
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基于变换域的条带噪声去除方法

为解决线扫描图像中的条带噪声干扰问题,提出了傅里叶变换与小波分解相结合的变换域条带噪声去除方法。首先对图像进行多尺度小波分解,将包含条带噪声的小波子带与包含图像信息的小波子带分离;然后对含有条带噪声的小波子带进行傅里叶变换,并对变换系数进行带阻滤波以消除条带噪声。利用实际采集的带有条带噪声的棉花异性纤维图像进行仿真实验,结果表明:傅里叶变换与小波分解相结合的方法,去噪效果明显优于单独使用傅里叶变换或小波分解的方法,既能有效地去除图像中的条带噪声,又能较好地保持图像的细节信息。     

一种去除遥感图像条带噪声的小波矩匹配方法

遥感图像条带噪声掩盖数据真实的辐射信息,降低图像的质量及应用效果。针对窗口矩匹配算法对灰度分布不均一影像的条带噪声去除不彻底问题,提出小波矩匹配条带噪声去除算法。首先,基于小波多分辨率的特性将低频小波系数和高频小波系数分离;其次,利用移动窗口矩匹配算法对影像低频成分滤波,利用阈值法去除影像高频成分中的条带噪声;最后,通过小波逆变换重构得到去条带后的影像。采用局部峰值信噪比、局部结构相似度、模糊系数和拟合优度对影像去噪效果进行定量评价。结果表明:小波矩匹配算法的各项指标均优于矩匹配、小波软阈值和窗口矩匹配算法。小波矩匹配算法改善了窗口矩匹配算法要求影像灰度分布均匀的局限性,结合空间域和频率域去噪的优势,在去除条带噪声的同时能够较好地保留影像的细节信息。     

分段线性动态矩匹配条带去除

由于探测器之间对接收的地物辐射信号的响应特征不同,导致遥感数据含有条带噪声,严重影响了图像质量及后续的定量计算。针对探测器响应函数在图像低值区及高值区呈非线性的特点,在着重分析矩匹配方法的基础上,提出分段线性动态矩匹配条带去除方法。方法设定阈值分割高中低值域统计区间,对探测器响应函数进行分段线性拟合,并对探测器每一分图像动态采用其领域内均值和标准差作为参考值进行条带纠正。应用TM数据第4波段及环境一号卫星高光谱数据进行去条带实验,并定性和定量地比较了该方法与动态矩匹配、傅里叶变换、自动均衡化曲线方法的去条带效果。结果表明该方法能够在保留图像基本信息的前提下,获得最佳的去条带效果,尤其能够提高非均匀地物分布区域内水体的条带去除效果。     

GA-BP网络在去除MODIS遥感影像条带噪声中的应用

由于卫星传感器光、电器件在反复扫描地物的成像过程中受扫描探测单元正反扫描响应差异、传感器机械运动和温度变化等因素的影响,使MODIS(moderate-resolution imaging spectroradiometer)遥感影像产生一定的条带噪声。影像条带噪声的去除在改善MODIS数据的质量和提高其利用率上具有重要的意义。在BP神经网络去除MODIS遥感影像条带噪声方法的基础上,运用遗传算法GA优化BP神经网络,有效地提高了神经网络的学习、训练速度和精准度,去除了影像的条带噪声,取得良好的实验结果。     

多光谱遥感图像去云方法

云覆盖是遥感图像解译过程中常遇到的问题。本文在介绍了几种常用于多传感器遥感图像去云方法的基础上,探讨了基于精度融合的频率域高通滤波去云方法,并对ETM图像进行去云处理,结果表明这种方法具有很好的去云效果,同时计算简便,此外这种方法还具有很强的普适性。     

An automated de‐striping algorithm for Ocean Colour Monitor imagery

Ocean Colour Monitor, OCM, is a push broom sensor design with 3740 detectors across scan for each of its eight channels. Along track striping caused by poorly characterised detector to detector calibration results in derived ocean colour products that are hardly usable. Because OCM is turned on only for purchased orbits, the coefficients needed to correct each cross scan detector are not constant for all orbits. An algorithm has been developed to remove the effects of striping which is based on the information contained in each image. The algorithm performance depends only on the quantity of contiguous pixels over the water for each detector. Along scan detectors that have sufficient water pixels within the image to derive statistically significant correction coefficients are corrected with a fixed vector of scale factors. This paper outlines the automated de‐striping algorithm (ADM), shows the results of ADM, quantifies the improvement to the images achieved and describes the limitations of the method.     

A new generation of Chinese FY-3C microwave sounding measurements and the initial assessments of its observations

A new Chinese satellite, FY-3C, carrying second-generation microwave sounding instruments, was introduced and the observations were evaluated. On board FY-3C are the Microwave Temperature Sounder ?2 (MWTS-2) and Microwave Humidity Sounder?2 (MWHS-2), and these devices are more advanced than those on board the previous satellites, FY-3A/B; the spectral and spatial resolutions of the devices are also higher than the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS). The 50–60 GHz and 183 GHz sounding abilities are similar to Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-orbiting Partnership (SNPP). FY-3C and SNPP were launched in different orbits but are complementary in observation area. In addition, MWHS-2 is the first operational polar-orbiting, satellite-based sensor which has 118 GHz channels. Assessment of MWTS-2 and MWHS-2 observations indicate that striping noises are apparent from the observations. A technique combining principal component analysis (PCA) with Ensemble Empirical Mode Decomposition (EEMD) is used to extract the noise. The striping noise in the first PCA component is extracted from the data. It was determined that the bias and standard deviation (SD) of MWTS-2 noise was larger than that of the ATMS corresponding channels. The biases and SDs of the MWHS-2 noise were comparable in magnitude with ATMS moisture channels. After removing the noise, the biases and SDs of the difference between observation and simulation (O–B) for MWTS-2 were slightly larger than the ATMS. Biases of MWHS-2 were close to the ATMS. The O–B biases of MWHS-2 118 GHz channels were within the range from ?0.6 to 0.2 K, and the SDs were approximately 0.5–1.2 K.     

A simple and effective method for filling gaps in Landsat ETM+ SLC-off images

The scan-line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed in 2003, resulting in about 22% of the pixels per scene not being scanned. The SLC failure has seriously limited the scientific applications of ETM+ data. While there have been a number of methods developed to fill in the data gaps, each method has shortcomings, especially for heterogeneous landscapes. Based on the assumption that the same-class neighboring pixels around the un-scanned pixels have similar spectral characteristics, and that these neighboring and un-scanned pixels exhibit similar patterns of spectral differences between dates, we developed a simple and effective method to interpolate the values of the pixels within the gaps. We refer to this method as the Neighborhood Similar Pixel Interpolator (NSPI). Simulated and actual SLC-off ETM+ images were used to assess the performance of the NSPI. Results indicate that NSPI can restore the value of un-scanned pixels very accurately, and that it works especially well in heterogeneous regions. In addition, it can work well even if there is a relatively long time interval or significant spectral changes between the input and target image. The filled images appear reasonably spatially continuous without obvious striping patterns. Supervised classification using the maximum likelihood algorithm was done on both gap-filled simulated SLC-off data and the original “gap free” data set, and it was found that classification results, including accuracies, were very comparable. This indicates that gap-filled products generated by NSPI will have relevance to the user community for various land cover applications. In addition, the simple principle and high computational efficiency of NSPI will enable processing large volumes of SLC-off ETM+ data.     

Suspended sediment concentrations in the Yangtze River estuary retrieved from the CMODIS data

The Chinese Moderate Resolution Imaging Spectroradiometer (CMODIS) was loaded on the China's SZ‐3 spacecraft. Using an empirical line method, the CMODIS radiance is converted to the water‐leaving reflectance, and is applied to inversion of the suspended sediment concentrations in the Yangtze River estuary. The concentrations ranging between 0 mg/L and 1000 mg/L are well validated by the field measurement data. This study demonstrates an example for the feasibility of the CMODIS data for concentration retrieval of the suspended sediment.     

Performance of a Mass-Storage System for Video-on-Demand

Advancements in storage technology along with the fast deployment of high-speed networks has allowed the storage, transmission, and manipulation of multimedia information such as text, graphics, still images, video, and audio to be feasible. Our study focused on the performance of the mass storage system for a large-scale video-on-demand server. Different video file striping schemes, such as application level striping and device driver level striping, were examined in order to study scalability and performance issues. To study the impact of different concurrent access patterns on the performance of a server, experimental results were obtained on group access on a single video file and multiple group accesses on multiple video files. All of our experiments were conducted on a fully configured Silicon Graphics Inc. Onyx computer system. The Onyx machine was connected to 31 SCSI-2 channels which have 496 GBytes disk storage, 20 MIPS R4400 processors, and 768 MBytes main memory. From the experimental results, the storage system of Onyx machine can potentially provide about 360 concurrent video accesses with guaranteed quality of service.     

PoNet: A universal physical optimization-based spectral super-resolution network for arbitrary multispectral images

Spectral super-resolution is a very important technique to obtain hyperspectral images from only multispectral images, which can effectively solve the high acquisition cost and low spatial resolution of hyperspectral images. However, in practice, multispectral channels or images captured by the same sensor are often with different spatial resolutions, which brings a severe challenge to spectral super-resolution. This paper proposed a universal spectral super-resolution network based on physical optimization unfolding for arbitrary multispectral images, including single-resolution and cross-scale multispectral images. Furthermore, two new strategies are proposed to make full use of the spectral information, namely, cross-dimensional channel attention and cross-depth feature fusion. Experimental results on five data sets show superiority and stability of PoNet addressing any spectral super-resolution situations.     

Simulating Landsat ETM+ imagery using DAIS 7915 hyperspectral scanner data

Any application in remote sensing requires specific datasets that are defined in terms of the spectral and spatial characteristics of the sensor. The question as to which spatial resolution is preferable for a particular problem is a difficult one, and represents a critical decision in the planning of a project. Remote sensing data with different spatial resolutions are often required. In such cases, an alternative to the purchase of new images is simulation. Simulation is often used to generate images that will be acquired by a future sensor such as using aircraft imagery to simulate a new, as yet unavailable, orbital sensor. Simulation has usually been carried out as a spatial degradation process. However, to make a realistic simulation it is necessary to take a number of factors into consideration. Two such factors are the point spread function (PSF) and the spectral response of the sensor. In this study, Digital Airborne Imaging Spectrometer (DAIS 7915) data for a test site from the La Mancha Alta region of Spain are used to simulate a Landsat Enhanced Thematic Mapper Plus (ETM+) image. The results are promising in that comparable images are produced by considering the PSF and spectral response of the Landsat ETM+.     

适用于智能传感器系统的SVM集成研究

以支持向量机(SVM)为代表的人工智能技术在智能传感器系统中得到了广泛的应用,但传统的SVM有"灾难性遗忘"现象,即会遗忘以前学过的知识,并且不能增量学习新的数据,这已无法满足智能传感器系统实时性的要求。而Learn++算法能够增量地学习新来的数据,即使新来数据属于新的类,也不会遗忘已经学习到的旧知识。为了解决上述问题,提出了一种基于壳向量算法的Learn++集成方法。实验结果表明:该算法不但具有增量学习的能力,而且在保证分类精度的同时,提高了训练速度,减小了存储规模,可以满足当下智能传感器系统在线学习的需求。