共查询到20条相似文献，搜索用时 502 毫秒
1.
Kernel adaptive subspace detector for hyperspectral imagery 总被引：1，自引：0，他引：1
In this letter, we present a kernel-based nonlinear version of the adaptive subspace detector (ASD) that implicitly detects signals of interest in a high-dimensional (possibly infinite) feature space associated with a particular nonlinear mapping. In order to address the high dimensionality of the feature space, ASD is first implicitly formulated in the feature space, which is then converted into an expression in terms of kernel functions via the kernel trick property of the Mercer kernels. Experimental results based on simulated data and real hyperspectral imagery show that the proposed kernel-based ASD outperforms the conventional ASD and a nonlinear anomaly detector so called the kernel RX-algorithm. 相似文献
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一种遥感影像核变化检测方法 总被引：1，自引：0，他引：1
提出了一种新的遥感影像核变化检测方法。该方法是将原始空间不同时相的输入矢量通过核函数非线性映射到高维特征空间，然后在高维特征空间中通过传统变化检测方法处理得到新的输入矢量，最后通过半监督的单类支持向量机算法对新的输入矢量构造变化区域与非变化区域的最优分割超平面。试验证实，本文的核变化检测方法具有较高的检测精度和效率。 相似文献
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A Selective KPCA Algorithm Based on High-Order Statistics for Anomaly Detection in Hyperspectral Imagery 总被引：1，自引：0，他引：1
《Geoscience and Remote Sensing Letters, IEEE》2008,5(1):43-47
In this letter, a selective kernel principal component analysis (KPCA) algorithm based on high-order statistics is proposed for anomaly detection in hyperspectral imagery. First, KPCA is performed on the original hyperspectral data to fully mine the high-order correlation between spectral bands. Then, the average local singularity (LS) is defined based on the high-order statistics in the local sliding window, which is used as a measure for selecting the most informative nonlinear component for anomaly detection. By the selective KPCA, information on anomalous targets is extracted to maximum extent, and background clutters are well suppressed in the selected component. Finally, the selected component with maximum average LS is used as input for anomaly detectors. Numerical experiments are conducted on real hyperspectral images collected by the airborne visible/infrared imaging spectrometer. The results strongly prove the effectiveness of the proposed algorithm. 相似文献
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This letter describes the extension of signal subspace processing (SSP) to the arena of anomaly detection. In particular, we develop an SSP-based, local anomaly detector that exploits the rich information available in the multiple bands of a hyperspectral (HS) image. This SSP approach is based on signal processing considerations, and its entire formulation reduces to a straightforward (and intuitively pleasing) geometric and algebraic development. We extend the basic SSP concepts to the HS anomaly detection problem, develop an SSP HS anomaly detector, and evaluate this algorithm using multiple HS data files. 相似文献
6.
Hamidullah Binol 《Journal of the Indian Society of Remote Sensing》2018,46(10):1605-1615
This article presents a novel supervised target detection approach on hyperspectral images based on Fukunaga–Koontz Transform (FKT) with compositional kernel combination. The Fukunaga–Koontz Transform is one of the most effective techniques for solving problems that involve two-pattern characteristics. To capture nonlinear properties of data, researchers have extended FKT to kernel FKT (KFKT) by means of kernel machines. However, the performance of KFKT depends on choosing convenient kernel functions and/or selection of the proper parameter(s). In this work, instead of selecting a single kernel for nonlinear version of FKT, we have applied a compositional kernel combination approach to capture the underlying local distributions of hyperspectral remote sensing data. Optimal parameter selection for each kernel function is achieved applying an evolutionary technique called differential evolution algorithm. The proposed new nonlinear target detection algorithm is tested for hyperspectral images. The experimental results verify that the proposed target detection algorithm has effective and promising performance compared to the conventional version for supervised target detection applications. 相似文献
7.
目前,高光谱遥感特征提取方法往往因受到噪声的干扰而导致降维效果欠佳。近年来,判别局部对齐DLA (Discriminative Locality Alignment)由于可以处理非线性分布样本、保留局部判别信息,同时避免矩阵奇异性问题,受到了很多学者的关注;但该方法无法有效估计和减少噪声对高光谱遥感影像的影响。针对以上问题,本文提出了最小噪声判别局部对齐MDLA (Minimum-noise Discriminative Locality Alignment)的线性特征提取方法和核最小噪声判别局部对齐KMDLA (Kernel Minimum-noise Discriminative Locality Alignment)的非线性特征提取方法。充分利用最小噪声分离MNF (Minimum Noise Fraction)的去噪能力,将MNF与DLA算法结合提出了MDLA方法,该方法首先利用MNF对高光谱遥感影像进行降维,减少图像中的噪声,然后再在子空间进行DLA变换得到最终的投影数据。为提高样本分布的非线性判别能力,基于KMNF与DLA算法将核方法引入MDLA,提出了KMDLA方法,该方法首先通过KMNF将原始空间的数据映射到新的特征空间,然后在特征空间中进行DLA变换得到最终的投影数据。实验部分首先利用3组高光谱遥感数据对提出算法的性能进行评价,并与相关特征提取算法进行了对比分析,最后分析了图像噪声对不同降维方法性能的影响。结果表明:提出的算法对高光谱遥感影像特征提取效果较好,且可有效减少噪声对影像的影响并提升其分类准确度。 相似文献
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传统的基于鲁棒主成分分析的高光谱异常探测模型中,稀疏异常矩阵假设为非低秩且其非零元素满足随机分布条件。这导致稀疏矩阵的非零元素影响低秩背景矩阵的估计,进而制约背景信息和异常信息的有效分离。提出列式鲁棒主成分分析的异常探测方法,改进异常矩阵为列稀疏条件来解决上述问题。该方法分解高光谱影像2维矩阵为低秩背景矩阵,列稀疏异常矩阵和噪声矩阵,松弛目标方程为凸优化问题,并采用非精确增强拉格朗日乘子算法来求解得到列稀疏异常矩阵的最优估计。最后,对稀疏异常矩阵中所有列的L2范数值进行阈值分割来探测得到异常像元。利用两个高光谱影像数据集,对比5种主流的异常探测方法来验证提出方法的有效性。实验结果表明,列式鲁棒主成分分析方法优于包括传统鲁棒主成分分析模型在内的5种异常探测方法,且计算效率适中。 相似文献
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高光谱遥感影像具有丰富的光谱信息,在地物分类识别方面具有明显的优势。针对复杂高光谱影像分类问题,应用了一种广义判别分析特征提取技术。将输入样本通过非线性函数映射到特征空间,在特征空间中应用线性判别特征提取方法;算法求解过程中涉及到在特征空间的内积用核函数代替,简化计算的同时也使得算法与非线性函数的具体形式无关。通过影像分类试验表明,该方法较常用特征提取方法更有利于分类精度的提高。 相似文献
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非参数核方法由于采用统一的度量标准,在大数据中利用高维样本数据学习时容易遭遇维数灾难问题.挖掘高维空间中的低维几何特性,有助于揭示数据分布的流形结构,进而利用有限样本的高维数据在低维子空间逼近数据的真实分布.基于此,提出一种新的高维数据密度非参数估计的低维流形代表点法,通过从高维空间中挖掘数据分布的几何结构来估计密度.... 相似文献
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为了进一步提高基于独立分量分析ICA(Independent Component Analysis)的遥感图像变化检测精确度,更好地实现地表覆盖的动态监测,将多尺度几何分析和核独立分量分析KICA(Kernel Independent Component Analysis)相结合应用于遥感图像的地表覆盖变化检测。首先利用Contourlet变换、复Contourlet变换CCT(Complex Contourlet Transform)、非下采样Contourlet变换NSCT(Nonsubsampled Contourlet Transform)等多尺度几何分析对土地遥感图像进行多尺度分解;然后对分解后的数据进行核独立分量分析,通过核函数将数据映射到高维特征空间中,再在该空间中用ICA方法分离出互相独立的分量;最后将分离后的地表变化分量转化为图像分量,再采用最大类间方差法对变化图像进行分割,实现地表覆盖的变化检测。给出了本文方法和近年来提出的基于ICA、基于KICA、基于小波变换和ICA等变化检测方法的实验结果,并进行了分析和定量比较。实验结果表明,基于多尺度几何分析和KICA的变化检测方法能更好地分离出遥感图像的变化信息,其中基于NSCT和KICA方法的错判和漏检误差最小,且在边缘细节方面处理得更好,而基于Contourlet变换和KICA方法的检测效率相对较高。 相似文献
13.
ZHANG Jingxiong David Roy Sadashiva Devadiga ZHENG Min 《地球空间信息科学学报》2007,10(1):44-50
With remote sensing information products becoming increasingly varied and arguably improved, scientific applications of such products rely on their quality assessment. In an operational context such as the NASA (National Aeronautics and Space Administration) information production based on the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on board Earth Observing System (EOS) Terra and Aqua satellites, efficient ways of detecting product anomaly, i.e., to discriminate between product artifacts and real changes in Earth processes being monitored, are extremely important to assist and inform the user communities about potential unreliability in the products. A technique for anomaly detection, known as MAD (the median of absolute deviate from the median), in MODIS land products via time series analysis is described, which can handle intra- and in-ter-annual variation in the data by using MAD statistics of the original data and their first-order difference. This method is shown to be robust and work across major land products, including NDVI, active fire, snow cover, and surface reflectance, and its applicabil-ity to multi-disciplinary products is anticipated. 相似文献
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Standard least-squares collocation (LSC) assumes 2D stationarity and 3D isotropy, and relies on a covariance function to account
for spatial dependence in the observed data. However, the assumption that the spatial dependence is constant throughout the
region of interest may sometimes be violated. Assuming a stationary covariance structure can result in over-smoothing of,
e.g., the gravity field in mountains and under-smoothing in great plains. We introduce the kernel convolution method from
spatial statistics for non-stationary covariance structures, and demonstrate its advantage for dealing with non-stationarity
in geodetic data. We then compared stationary and non- stationary covariance functions in 2D LSC to the empirical example
of gravity anomaly interpolation near the Darling Fault, Western Australia, where the field is anisotropic and non-stationary.
The results with non-stationary covariance functions are better than standard LSC in terms of formal errors and cross-validation
against data not used in the interpolation, demonstrating that the use of non-stationary covariance functions can improve
upon standard (stationary) LSC. 相似文献
16.
传统截断核函数存在谱泄露问题,且实测数据在移去恢复频段的利用率低。本文以Hotine核函数为例引入了一种高低阶均修正的截断核函数,在其基础上进一步提出了仅低阶修正的截断核函数,具体包含余弦修正和线型修正两种类型。修正核函数能够有效地控制截断核函数存在的谱泄露问题,并且增大了实测数据在修正频段对高程异常的贡献率。试验结果表明,当低阶修正带宽一定时,低阶修正核函数计算的似大地水准面精度优于传统截断核函数计算的似大地水准面精度,并且与高低阶均修正的核函数的解算精度相当。但在计算效率上,低阶修正核函数明显优于高低阶均修正的核函数。本文的试验证实了在基于Helmert第二压缩法的边值问题(Stokes-Helmert或Hotine-Helmert边值问题)中低阶修正核函数是一种比较有效的核函数。 相似文献
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Ma Guorui Sui Haigang Wang Wenlong Wu Chun 《Journal of the Indian Society of Remote Sensing》2017,45(5):749-757
This paper presents a kernel-based approach for the change detection of remote sensing images. It detects change by comparing the probability density (PD), expressed as kernel functions, of the feature vector extracted from bi-temporal images. PD is compared by defined kernel functions without immediate PD estimation. This algorithm is model-free and it can process multidimensional data, and is fit for the images with rich texture in particular. Experimental results show that overall accuracy of the algorithm is 98.9%, a little bit better than that of the change vector analysis and classification comparison method, which is 96.7 and 95.9% respectively. 相似文献
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基于再生核Hilbert空间小波核函数支持向量机的高光谱遥感影像分类 总被引：1，自引：0，他引：1
针对支持向量机用于高光谱遥感影像分类存在的分类精度不高、参数选择困难等问题,提出一种再生核Hilbert空间的小波核.其可以逼近任意非线性函数,能够有效改进参数估计的效果,进而实现基于再生核Hilbert空间的小波核函数支持向量机(小波支持向量机).并选取北京昌平地区的国产高光谱数据operational modula... 相似文献
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Michael L. Humber Luigi Boschetti Louis Giglio Christopher O. Justice 《International Journal of Digital Earth》2019,12(4):460-484
We characterize the agreement and disagreement of four publically available burned products (Fire CCI, Copernicus Burnt Area, MODIS MCD45A1, and MODIS MCD64A1) at a finer spatial and temporal scale than previous assessments using a grid of three-dimensional cells defined both in space and in time. Our analysis, conducted using seven years of data (2005–2011), shows that estimates of burned area vary greatly between products in terms of total area burned, the location of burning, and the timing of the burning. We use regional and monthly units for analysis to provide insight into the variation between products that can be lost when considering products yearly and/or globally. Comparison with independent, contemporaneous MODIS active fire observations provides one indication of which products most reasonably capture the burning regime. Our results have implications for the use of global burned area products in fire ecology, management and emissions applications. 相似文献
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提出了一种基于核函数度量相似性的遥感影像变化检测算法。该算法通过比较两个时相特征向量的概率密度进行变化判别,将概率密度的比较转化成核函数的形式,利用核函数的相似度量功能进行变化判别,通过指定的核函数避开概率密度的估计,达到概率密度比较的目的。 相似文献