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高光谱遥感影像数据量大、波段数多,容易导致“维数灾难”。传统流形学习方法一般仅考虑其光谱特征,忽略了空间信息。为此提出一种非监督的基于加权空-谱联合保持嵌入(WSCPE)的维数约简算法。首先采用加权均值滤波(WMF)方法对高光谱影像进行滤波,以消除噪点和背景点的干扰。然后根据遥感影像地物分布的空间一致性,通过采用加权空-谱联合距离(WSCD)来融合像素点的光谱信息和空间信息,有效选取各像素点的空-谱近邻,并根据像素点与其空-谱近邻点之间的坐标距离来有区别的利用其近邻点进行流形重构,提取低维鉴别特征进行地物分类。在PaviaU和Indian Pines数据集上的分类结果表明,总体分类精度分别达到了98.89%和95.47%。该方法在反映影像内部流形结构的同时,有效融合了影像的空间-光谱信息,故能提高影像特征的鉴别性,并提升分类性能。 相似文献
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本文提出了一种基于张量子空间的多维滤波算法,将其应用于高光谱遥感影像降噪。该方法将高光谱影像数据视为三阶张量,引入张量数据表达,通过张量子空间分解将含噪影像投影到信号子空间,根据影像信号与噪声在子空间中分布的不同滤除噪声并保留原始影像的信号成分。利用该算法作用于多组含噪高光谱数据,对比逐波段二维维纳滤波算法、小波降噪算法等传统数字图像降噪算法的结果,实验证明了这种新型降噪算法的有效性。 相似文献
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A generalized multidimensional Wiener filter for denoising is adapted to hyperspectral images (HSIs). multidimensional wiener filtering (MWF) uses the signal subspace of each n-mode flattening matrix of the HSI, which is a third-order tensor. However, in the HSI case, the n-mode ranks are close to the n-mode dimensions. Thus, the signal subspace dimension can be underestimated. This leads to a loss of spatial resolution-edge blurring-and artifacts in the restored HSI. To cope with the underestimation while preserving edges, a new method is proposed. It estimates the relevant directions of flattening that may not be parallel to HSI dimensions. We adapt the bidimensional straight line detection algorithm that estimates the HSI main directions, which are used to flatten the HSI tensor. We also generalize the quadtree decomposition to tensors in order to adapt the filtering to the local image characteristics. Comparative studies with MWF, principal component analysis-stationary wavelet transform, and channel-by-channel Wiener filtering show that our algorithm provides better performance while restoring impaired HYDICE HSIs. 相似文献
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小样本的高光谱图像降噪与分类 总被引：1，自引：0，他引：1
在样本数目稀少情况下实现高光谱图像精细分类是个挑战性的问题。高光谱图像信噪比提高比较困难,噪声大小对分类结果有最直接的影响。利用高光谱图像相邻波段之间的相关性和相邻像素之间的相关性,提出多级降噪滤波的高光谱图像分类方法,通过改进的两阶段稀疏与低秩矩阵分解方法,去除高光谱图像中能量较高的噪声,利用主成分分析方法去除高光谱图像中能量较低的噪声,引导滤波方法去除分类结果图中的"椒盐噪声"。选取两幅真实高光谱图像进行实验,结果表明,两阶段稀疏与低秩矩阵分解法和主成分分析法两种降噪方法具有较强的互补性;引导滤波方法使得分类图更加平滑且分类精度更高。与其他光谱空间分类方法相比,本文方法分类精度更高,且在样本极少时能获得很高的分类精度。 相似文献
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传统依据图嵌入的高光谱图像维数约简算法多数仅利用光谱信息表征像元间单一关系,忽视了数据间的多元几何结构。本文提出了一种面向高光谱图像分类的空-谱协同正则化稀疏超图嵌入算法(SSRSHE)。该算法首先利用稀疏表示揭示像元之间的相关性,自适应选择近邻,并构建稀疏本征超图和惩罚超图,以有效表征像元间的复杂多元关系,并进行正则化处理。然后利用遥感图像空间一致性原则,计算局部空间邻域散度来保持样本局部邻域结构,并引入样本总体散度来保持高光谱数据的整体结构。在低维嵌入空间中,尽可能使类内数据聚集、类间数据远离,提取鉴别特征用于分类。在Indian Pines和PaviaU高光谱遥感数据集上试验结果表明,本文算法总体分类精度分别达到86.7%和 92.2%。相比传统光谱维数约简算法,该算法可有效改善高光谱图像地物分类性能。 相似文献
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Hyperspectral image (HSI) and multispectral image (MSI) are two types of images widely used in the field of remote sensing. These images are useful in certain applications, such as environmental monitoring, target detection, and mineral exploration. HSI contains a large amount of spectral information. Photons are typically collected in a larger spatial area on the sensor to ensure a sufficiently high signal-to-noise ratio (SNR). Accordingly, the HSI spatial resolution is much lower compared with MSI. This low spatial resolution greatly affects the practicality of HSI. Accordingly, fusing a low-spatial resolution HSI (LR-HSI) with a high-spatial resolution MSI (HR-MSI) in the same scene to obtain a high-resolution HSI (HR-HSI) is a method for solving such problems, which resolves the contradiction that the spatial resolution and the spectral resolution cannot simultaneously maintain a high level. From the analysis of fusion effect, the spatial and spectral reconstruction errors of the existing algorithms are mainly reflected in the edge and detail areas. The method proposed in this work was a fusion algorithm for dictionary construction and image reconstruction based on detail attention. In terms of maintaining spectral characteristics, the spectral distribution in the detail area is complex and diverse because of the proximity effect of the image. This work proposes to perform dictionary learning on the image and detail layers. The detail perception error terms and a constraint of edge adaptive directional total variation are proposed for spatial characteristic enhancement, which is combined with a local low rank constraint in the same fusion framework to estimate the sparse coefficient. Experiments were conducted on two datasets, namely, Pavia University and Indian Pine, to verify the effectiveness of the proposed method. The quantitative evaluation metrics contain peak SNR, relative dimensionless global error in synthesis, spectral angle map, and universal image quality index. Based on the experimental comparison, the fusion result of the algorithm proposed in this work is significantly improved compared with those of the other algorithms in terms of spatial and spectral characteristics. This work uses dictionary learning to propose a fusion algorithm for dictionary construction and image reconstruction with attention to details through the analysis of the existing hyperspectral and multispectral image fusion algorithms. A hierarchical dictionary learning algorithm is proposed to address the problem of large reconstruction error in the detail part of the existing algorithms. The detail perception error term and the direction adaptive full variational regularization term are used to improve the spectral dictionary solution and coefficient estimation, respectively. The result of the fusion is the error in the spectral characteristics and spatial texture of the detail, which achieves an accurate representation of the edge detail. © 2022 National Remote Sensing Bulletin. All rights reserved. 相似文献
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受仪器和观测条件限制,高光谱数据易受噪声污染,给数据解译带来挑战。针对传统稀疏解混模型抗噪性能差的问题,本文提出一种截断加权核范数稀疏解混方法,利用高光谱图像像元之间的相关性减轻噪声对丰度估计的干扰。该方法借助低秩表示在挖掘数据内在低维结构方面的优势,在稀疏解混中加入基于截断加权核范数的低秩约束,并结合加权稀疏技术,在稀疏正则项中引入空间邻域权重。截断加权核范数对丰度矩阵的奇异值向量分段处理,可以更好地实现丰度矩阵的低秩逼近,使丰度图像保持空间一致性并保留更多细节信息,空间加权策略则增强了丰度图像的空间连续性。模拟高光谱数据、Cuprite矿区真实数据和红树林高光谱数据实验表明,与其他先进的稀疏解混方法相比,所提方法具有更好的抗噪性,能够提高解混精度。 相似文献
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结合Gram-Schmidt变换的高光谱影像谐波分析融合算法 总被引：1，自引：0，他引：1
针对高光谱影像谐波分析融合(HAF)算法在影像融合时不顾及地物光谱曲线整体反射率这一缺陷,提出了结合Gram-Schmidt变换的高光谱影像谐波分析融合(GSHAF)改进算法。GSHAF算法可在完全保留融合前后像元光谱曲线波形形态的基础上,将高光谱影像融合简化为各像元光谱曲线的谐波余相组成的二维影像与高空间分辨率影像之间的融合。它是在原始高光谱影像光谱曲线被谐波分解为谐波余项、振幅和相位后,首先将其谐波余项与高空间分辨率影像进行GS变换融合,这样便可有效地修正融合后像元光谱曲线的反射率特征,随后再利用该融合影像与谐波振幅、相位进行谐波逆变换,完成高光谱影像谐波融合。本文最后利用Hyperion高光谱遥感影像与ALI高空间分辨率影像对GSHAF算法进行可行性分析,再以HJ-1A等卫星数据对其进行普适性验证,试验结果表明,GSHAF算法不仅可以完全地保留光谱曲线波形形态,而且融合后影像的地物光谱曲线反射率更接近真实地物。 相似文献
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超光谱图像在常规的二维图像中加入了光谱维度,具有更大的信息量的同时也带来了较大的光谱冗余性,这给图像压缩带来了新的挑战。提出了一种基于张量分解的超光谱图像降秩与压缩方法,将超光谱图像视为三阶张量数据表示,并使用张量分解技术将原始观测张量分解为核张量与多个投影矩阵的乘积形式。这样,超光谱图像被压缩为了低秩张量,它可以通过张量反投影进行图像重构。实验证明张量分解技术能够将超光谱图像压缩到很低的比率,同时保持较低的重构相对误差。 相似文献
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In automated remote sensing based image analysis, it is important to consider the multiple features of a certain pixel, such as the spectral signature, morphological property, and shape feature, in both the spatial and spectral domains, to improve the classification accuracy. Therefore, it is essential to consider the complementary properties of the different features and combine them in order to obtain an accurate classification rate. In this paper, we introduce a modified stochastic neighbor embedding (MSNE) algorithm for multiple features dimension reduction (DR) under a probability preserving projection framework. For each feature, a probability distribution is constructed based on t-distributed stochastic neighbor embedding (t-SNE), and we then alternately solve t-SNE and learn the optimal combination coefficients for different features in the proposed multiple features DR optimization. Compared with conventional remote sensing image DR strategies, the suggested algorithm utilizes both the spatial and spectral features of a pixel to achieve a physically meaningful low-dimensional feature representation for the subsequent classification, by automatically learning a combination coefficient for each feature. The classification results using hyperspectral remote sensing images (HSI) show that MSNE can effectively improve RS image classification performance. 相似文献
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Hyperspectral images (HSI) provide a new way to exploit the internal physical composition of the land scene. The basic platform for acquiring HSI data-sets are airborne or spaceborne spectral imaging. Retrieving useful information from hyperspectral images can be grouped into four categories. (1) Classification: Hyperspectral images provide so much spectral and spatial information that remotely sensed image classification has become a complex task. (2) Endmember extraction and spectral unmixing: Among images, only HSI have a complete model to represent the internal structure of each pixel where the endmembers are the elements. Identification of endmembers from HSI thus becomes the foremost step in interpretation of each pixel. With proper endmembers, the corresponding abundances can also be exactly calculated. (3) Target detection: Another practical problem is how to determine the existence of certain resolved or full pixel objects from a complex background. Constructing a reliable rule for separating target signals from all the other background signals, even in the case of low target occurrence and high spectral variation, comprises the key to this problem. (4) Change detection: Although change detection is not a new problem, detecting changes from hyperspectral images has brought new challenges, since the spectral bands are so many, accurate band-to-band correspondences and minor changes in subclass land objects can be depicted in HSI. In this paper, the basic theory and the most canonical works are discussed, along with the most recent advances in each aspect of hyperspectral image processing. 相似文献
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Haimiao Ge Liguo Wang Cheng Li Yanzhong Liu Ruixin Chen 《Journal of the Indian Society of Remote Sensing》2018,46(3):367-376
In this paper, an improved version of locally linear Embedding is proposed. In the proposed method, spectral correlation angle is invited to describe the distance between data points, which is expected to fit the hyperspectral image (HSI). The neighborhood graph of the data points is constructed based on supervised method. Different from traditional supervised feature extraction methods, the weight factors, which are used to control the transform, are adaptively achieved. In this way, the input arguments of original algorithm are not increased. To justify the effectiveness of the proposed method, experiments are conducted on two HSIs. Results show that the proposed method can improve the separability of HSI especially in low dimensions. 相似文献
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利用超平面最小方案,针对高光谱数据在空间维和光谱维建立能量函数,通过两个权重系数调节空间维数据曲面光滑程度和光谱曲线光滑程度,达到联合抑制噪声的目的。实验中,对Hamamatsu相机和AVIRIS采集的高光谱影像数据中比较严重的噪声污染,该方法有效地降低了噪声的影响,在AVIRIS水吸收带处的去噪效果尤为明显。 相似文献
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Recently, some tensor decomposition-based algorithms are proposed and performed well for hyperspectral anomaly detection (AD). This paper proposes a tensor decomposition-based local Mahalanobis-distance (Tensor-LMD) method for hyperspectral AD. First, a three-order tensor is employed to represent hyperspectral data-set and the Tucker decomposition technology is used to decompose such tensor into a core tensor and three factor matrices. Then, the minor PCs are used to eliminate anomaly and noise information along each mode and the more pure background data-set is obtained. Finally, the sliding dual-window strategy is used for both the background data-set and the original hyperspectral data-set, and the local Mahalanobis-distance detector is employed for the final results. The experimental results demonstrate that the proposed Tensor-LMD can achieve a better performance when compared with the comparison algorithms. 相似文献
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融合光谱-空间信息的高光谱遥感影像增量分类算法 总被引：1，自引：1，他引：0
提出了一种融合光谱和空间结构信息的高光谱遥感影像增量分类算法INC_SPEC_MPext。通过主成分分析(PCA)提取高光谱影像的若干主成分,利用数学形态学提取各主分量影像对应的形态学剖面(MP),再将所有主分量影像的形态学剖面归并联结,组成扩展的形态学剖面(MPext)。将MPext与光谱信息相结合以增加知识,最大限度地挖掘未标记样本的有用信息,优化分类器的学习能力。不断从分类器对未标记样本的预测结果中甄选置信度高的样本加入训练集,并迭代地利用扩大的训练集进行分类器构建和样本预测。以不同地表覆盖类型的AVIRIS Indian Pines和Hyperion EO-1Botswana作为测试数据,分别与基于光谱、MPext、光谱和MPext融合的分类方法进行比对。试验结果表明,在训练样本数量有限情况下,INC_SPEC_MPext算法在降低分类成本的同时,分类精度和Kappa系数都有不同程度的提高。 相似文献
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常规高光谱影像逐像素分类往往没有考虑空间相关性,分类结果未体现地物的空间关联和分布特征。为了在分类中充分利用空间特征,利用聚类信息并结合隐马尔可夫随机场模型讨论了高光谱遥感影像光谱-空间分类方法。首先,在不同特征提取方法（最小噪声分离、独立成分分析和主成分分析）下,使用不同聚类方法（k-均值、迭代自组织分析算法和模糊c-均值算法）借助隐马尔可夫随机场获取优化的分割图;然后,采用4连通区域标记法对分割区域标记生成图像对象,并根据支持向量机的逐像素分类结果采用多数投票法对图像对象进行分类;最后,借助凹槽窗口邻域滤波技术改进分类结果,削弱“椒盐”现象。该方法综合了监督分类和非监督分类的优势,通过聚类引入地物空间相关性信息,通过隐马尔可夫随机场引入上下文特征,较好地弥补了单纯基于光谱信息分类的不足。 相似文献