Edge-preserving-based collaborative representation for spectral-spatial classification

Recently, the nearest regularized subspace (NRS) classifier and its spectral–spatial versions such as joint collaborative representation (JCR) and weighted JCR (WJCR) have gained an increasing interest in the hyperspectral image classification. JCR and WJCR average each pixel with its neighbours in a spatial neighbourhood window. The use of spatial information as averaging of pixels in a local window may degrade the classification accuracy in the neighbourhood of discontinuities and class boundaries. We propose the edge-preserving-based collaborative representation (EPCR) classifier in this article, which overcomes this problem by using the edge image estimated by the original full-band hyperspectral image. The estimated edge image is used for calculation of the weights of neighbours and also the final residuals in the collaborative representation classifier. The advantage of multiscale spatial window is also assessed in this work. Moreover, the kernelized versions of NRS and its improved versions are developed in this article. Our experimental results on several popular hyperspectral images indicate that EPCR and its kernelized version are superior to some state-of-the-art classification methods.     

膜性肾病诊断的高光谱图像张量嵌入分析

目的 高光谱成像技术因其能够获取目标的详细空间和光谱信息,在医学领域引起了广泛关注。然而,对于识别任务来说,高光谱图像的高维特征通常会导致分类器性能不佳。因此,降维在高光谱图像分析过程中至关重要。为了在低维空间中保留医学高光谱图像的多流形结构信息并增强特征判别能力,本文提出了一种基于张量表示的拉普拉斯稀疏低秩图嵌入方法（tensor-based Laplacian regularized sparse and low-rank graph,T-LapSLRG）,用于医学高光谱图像的判别分析。方法 在T-LapSLRG中,基于有标签的张量样本,通过引入稀疏、低秩约束及流形正则项以构造监督张量图。张量表示用于捕获空间结构信息,稀疏和低秩约束用于保留局部和全局结构信息,流形正则项用于利用固有的几何信息并增强特征判别能力。通过引入张量图嵌入技术获取数据的低维特征并输入分类器以实现数据的分类及识别。结果 实验数据采用膜性肾病数据集,通过降维方法获取数据的低维特征,使用支持向量机（support vector machine,SVM）分类器对获取的低维特征进行分类。将T-LapSLRG获得的实验结果与相关的降维方法获得的实验结果进行性能比较,以证明T-LapSLRG算法的有效性。采用4个性能指标,即各个类别的准确性、总体准确性（overall accuracy,OA）、平均准确性（average accuracy,AA）和Kappa系数衡量分类性能。T-LapSLRG在膜性肾病数据集下的OA为97.14%,AA为97.05%,Kappa为0.942,各项性能指标均优于对比方法。其中,OA高出1.40%~34.75%,AA高出1.46%~36.89%,Kappa高出0.031~0.73。此外,通过T-LapSLRG算法获得的各个患者的分类准确率均达到90%以上。结论 T-LapSLRG算法在膜性肾病诊断中具有潜在临床价值。     

Random selection-based adaptive saliency-weighted RXD anomaly detection for hyperspectral imagery

With recent advances in hyperspectral imaging sensors, subtle and concealed targets that cannot be detected by multispectral imagery can be identified. The most widely used anomaly detection method is based on the Reed–Xiaoli (RX) algorithm. This unsupervised technique is preferable to supervised methods because it requires no a priori information for target detection. However, two major problems limit the performance of the RX detector (RXD). First, the background covariance matrix cannot be properly modelled because the complex background contains anomalous pixels and the images contain noise. Second, most RX-like methods use spectral information provided by data samples but ignore the spatial information of local pixels. Based on this observation, this article extends the concept of the weighted RX to develop a new approach called an adaptive saliency-weighted RXD (ASW-RXD) approach that integrates spectral and spatial image information into an RXD to improve anomaly detection performance at the pixel level. We recast the background covariance matrix and the mean vector of the RX function by multiplying them by a joint weight that in fuses spectral and local spatial information into each pixel. To better estimate the purity of the background, pixels are randomly selected from the image to represent background statistics. Experiments on two hyperspectral images showed that the proposed random selection-based ASW RXD (RSASW-RXD) approach can detect anomalies of various sizes, ranging from a few pixels to the sub-pixel level. It also yielded good performance compared with other benchmark methods.     

RX architectures for real-time anomaly detection in hyperspectral images

In the field of hyperspectral image processing, anomaly detection (AD) is a deeply investigated task whose goal is to find objects in the image that are anomalous with respect to the background. In many operational scenarios, detection, classification and identification of anomalous spectral pixels have to be performed in real time to quickly furnish information for decision-making. In this framework, many studies concern the design of computationally efficient AD algorithms for hyperspectral images in order to assure real-time or nearly real-time processing. In this work, a sub-class of anomaly detection algorithms is considered, i.e., those algorithms aimed at detecting small rare objects that are anomalous with respect to their local background. Among such techniques, one of the most established is the Reed–Xiaoli (RX) algorithm, which is based on a local Gaussian assumption for background clutter and locally estimates its parameters by means of the pixels inside a window around the pixel under test (PUT). In the literature, the RX decision rule has been employed to develop computationally efficient algorithms tested in real-time systems. Initially, a recursive block-based parameter estimation procedure was adopted that makes the RX processing and the detection performance differ from those of the original RX. More recently, an update strategy has been proposed which relies on a line-by-line processing without altering the RX detection statistic. In this work, the above-mentioned RX real-time oriented techniques have been improved using a linear algebra-based strategy to efficiently update the inverse covariance matrix thus avoiding its computation and inversion for each pixel of the hyperspectral image. The proposed strategy has been deeply discussed pointing out the benefits introduced on the two analyzed architectures in terms of overall number of elementary operations required. The results show the benefits of the new strategy with respect to the original architectures.     

GPU-based fast hyperspectral image classification using joint sparse representation with spectral consistency constraint

Due to the fact that neighboring hyperspectral pixels often belong to the same class with high probability, spatial correlation between pixels has been widely used in hyperspectral image classification. In this paper, a novel joint sparse representation classifier with spectral consistency constraint (JSRC-SCC) is proposed. Specifically, to efficiently exploit contextual structure information, a local adaptive weighted average value is reallocated as the central pixel of a window through spatial filtering, and then, representation coefficients are estimated by the joint sparse representation model, which is imposed by the spectral consistency constraint under \(\textit{l}_1\)-minimization. For the purpose of fast classification, graphics processing units are adopted to accelerate this model. Experimental results on two classical hyperspectral image data sets demonstrate the proposed method can not only produce satisfying classification performance, but also shorten the computational time significantly.     

高光谱图像分类的融合分层深度网络联合稀疏表示算法

在高光谱图像分类领域中每个像素的局部邻域一旦包含来自不同类别的样本,联合稀疏表示将受邻域内字典原子与测试样本之间同谱异类的影响,严重降低分类性能.根据高光谱图像的特点,文中提出融合分层深度网络的联合稀疏表示算法.在光谱和空间特征学习之间交替提取判别性光谱信息和空间信息,构建兼具空谱特征的学习字典,用于联合稀疏表示.在分类过程中将学习字典与测试样本间的相关系数与分类误差融合并决策.在两个高光谱遥感数据集上的实验验证文中算法的有效性.     

局部投影可分离的高光谱图像异常检测

针对高光谱异常检测中临近异常像素相互干扰和背景地物复杂的问题,提出基于局部投影可分离的高光谱图像异常检测算法.在归一化的数据中,将待测像素光谱作为参考光谱,构造目标子空间,然后把邻域背景像素投影到该子空间,用投影后向量模值构造异常度计算式.最后将检测到的异常与全局主要背景地物进行比对,肖除部分虚警.利用HyMap高光谱数据进行仿真实验结果表明,本文算法具有克服背景复杂性和干扰点的影响,尤其对异类干扰点的抑制效果更佳.     

基于多视图边界判别投影的高光谱图像分类

高光谱图像分类是遥感领域研究的热点问题,其关键在于利用高光谱图谱合一的 优势,同时融合高光谱图像中各个像元位置的光谱信息和空间信息,提高光谱图像分类精度。 针对高光谱图像特征维数高和冗余信息多等问题,采用多视图子空间学习方法进行特征降维, 提出了图正则化的多视图边界判别投影算法。将每个像元处的光谱特征看作一个视图,该像元 处的空间特征看作另一个视图,通过同时优化每个视图上的投影方向来寻找最优判别公共子空 间。公开测试数据集上的分类实验表明,多视图学习在高光谱图像空谱融合分类方面具有显著 的优越性,在多视图降维算法中,该算法具有最高的分类准确性。     

Locally adaptive linear mixture model-based super-resolution land-cover mapping based on a structure tensor

Super-resolution land-cover mapping (SRM) is a technique for generating land-cover thematic maps with a finer spatial resolution than the input image. Linear mixture model-based SRM (LSRM) is applied directly to a remotely sensed image and is composed of a spatial term that integrates the land-cover spatial pattern prior information, a spectral term that assumes that the spectral signature of each mixed pixel is composed of a weighted linear sum of endmember spectral signatures within that pixel and a balance parameter that defines the weight of the spatial term. The traditional LSRM adopts an isotropic spatial autocorrelation model in the land-cover spatial term for different classes and a fixed balance parameter for the entire image, and ignores the image local properties. The class boundaries are at risk of oversmoothing and may be imprecise, and the homogeneous regions may be unsmoothed and contain speckle-like artefacts in the result. This study proposes a locally adaptive LSRM (LA-LSRM) that integrates image local properties to predict fine spatial resolution pixel labels. The structure tensor is applied to detect the image local information. The LA-LSRM spatial term is locally adaptive and is composed of an anisotropic spatial autocorrelation model in which the spatial autocorrelation orientations of different classes may vary. The LA-LSRM balance parameter is locally adaptive to the different regions of the image. Such parameter obtains a relatively large value when the fine-resolution pixel is located in the homogeneous region to remove speckle-like artefacts and a relatively small value when the fine-resolution pixel is at the class boundary to preserve the edge. The LA-LSRM performance was assessed using a simulated multi-spectral image, an IKONOS multi-spectral image, a hyperspectral image produced by Airborne Visible/Infrared Imaging Spectrometer and a hyperspectral image produced by reflective optics system imaging spectrometer. Results show that the homogeneous regions were smoothed, the boundaries were better preserved and the overall accuracies were increased by LA-LSRM compared with traditional LSRM in all experiments.     

Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery

With increasing applications of hyperspectral imagery (HSI) in agriculture, mineralogy, military, and other fields, one of the fundamental tasks is accurate detection of the target of interest. In this article, improved sparse representation approaches using adaptive spatial support are proposed for effective target detection in HSI. For conventional sparse representation, an HSI pixel is represented as a sparse vector whose non-zero entries correspond to the weights of the selected training atoms from a structured dictionary. For improved sparse representation, spatial correlation and spectral similarity of adjacent neighbouring pixels are exploited as spatial support in this context. The size and shape of the spatial support is automatically determined using both adaptive window and adaptive neighbourhood strategies. Accordingly, a solution based on greedy pursuit algorithms is also given to solve the extended optimization problem in recovering the desired sparse representation. Comprehensive experiments on three different data sets using both visual inspection and quantitative evaluation are carried out. The results from these data sets have indicated that the proposed approaches help to generate improved results in terms of efficacy and efficiency.     

Hyperspectral anomaly detection using spectral–spatial features based on the human visual system

ABSTRACT

Anomaly detection (AD) is one of the most attracting topics within the recent 10 years in hyperspectral imagery (HSI). The goal of the AD is to label the pixels with significant spectral or spatial differences to their neighbours, as targets. In this paper, we propose a method that uses both spectral and spatial information of HSI based on human visual system (HVS). By inspiring the retina and the visual cortex functionality, the multiscale multiresolution analysis is applied to some principal components of hyperspectral data, to extract features from different spatial levels of the image. Then the global and local relations between features are considered based on inspiring the visual attention mechanism and inferotemporal (IT) part of the visual cortex. The effects of the attention mechanism are implemented using the logarithmic function which well highlights, small variations in pixels’ grey levels in global features. Also, the maximum operation is used over the local features for imitating the function of IT. Finally, the information theory concept is used for generating the final detection map by weighting the global and local detection maps to obtain the final anomaly map. The result of the proposed method is compared with some state-of-the-art methods such as SSRAD, FLD, PCA, RX, KPCA, and AED for two well-known real hyperspectral data which are San Diego airport and Pavia city, and a synthetic hyperspectral data. The results demonstrate that the proposed method effectively improves the AD capabilities, such as enhancement of the detection rate, reducing the false alarm rate and the computation complexity.     

基于加权K近邻和卷积神经网络的高光谱图像分类

传统的谱空联合分类算法通常定义一个邻域空间作为空间信息,忽略空间中非邻域空间信息,且容易将异类像元也考虑在内。针对于高光谱图像分类问题,提出了一种加权K近邻算法能够自适应地提取空间信息,首先定义光谱和空间坐标组成的特征空间,利用该特征空间寻找目标像元的K个相似像元,并对这些像元根据特征空间进行加权;将加权后的像元按照一定方式组合成三维张量表示最终的谱空联合信息,使用三维卷积神经网络对其进行训练,得到最终分类结果。从实验结果来看,相对于改进前的算法,在总体分类精度上得到了一定的提升,与原始的三维卷积神经网络相比,在收敛速度上也得到大大提升,为高光谱图像的谱空联合分类提供了一种更加实用的方法。     

A spectral-spatial method based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection

Recently, some methods based on low-rank and sparse matrix decomposition (LRASMD) have been developed to improve the performance of hyperspectral anomaly detection (AD). However, these methods mainly take advantage of the spectral information in hyperspectral imagery (HSI), and ignore the spatial information. This article proposes an LRASMD-based spectral-spatial (LS-SS) method for hyperspectral AD. First, the Go Decomposition (GoDec) algorithm is employed to solve the low-rank background component and the sparse anomaly component. Next, the sparse component is explored to calculate the spectral sparsity divergence index (SDI). Based on spectral SDI, the detection result in the spectral domain and the reliable background points, which are employed as training data to construct the background manifold by linear local tangent space alignment (LLTSA), can also be obtained. Then, based on the background manifold and the transformation matrix, the low-dimensional manifold of the whole data is computed by linear mapping. After that, the kernel collaborative representation detector (KCRD) is used in the low-dimensional manifold of the whole data for the spatial SDI. Finally, SS SDI is computed for the final detection result. The theoretical analysis and experimental results demonstrate that the proposed LS-SS can achieve better performance when compared with the comparison algorithms.     

Face recognition based on random subspace method and tensor subspace analysis

In this paper, we propose a novel method, called random subspace method (RSM) based on tensor (Tensor-RS), for face recognition. Different from the traditional RSM which treats each pixel (or feature) of the face image as a sampling unit, thus ignores the spatial information within the face image, the proposed Tensor-RS regards each small image region as a sampling unit and obtains spatial information within small image regions by using reshaping image and executing tensor-based feature extraction method. More specifically, an original whole face image is first partitioned into some sub-images to improve the robustness to facial variations, and then each sub-image is reshaped into a new matrix whose each row corresponds to a vectorized small sub-image region. After that, based on these rearranged newly formed matrices, an incomplete random sampling by row vectors rather than by features (or feature projections) is applied. Finally, tensor subspace method, which can effectively extract the spatial information within the same row (or column) vector, is used to extract useful features. Extensive experiments on four standard face databases (AR, Yale, Extended Yale B and CMU PIE) demonstrate that the proposed Tensor-RS method significantly outperforms state-of-the-art methods.

     

A probabilistic SVM approach for hyperspectral image classification using spectral and texture features

New hyperspectral sensors can collect a large number of spectral bands, which provide a capability to distinguish various objects and materials on the earth. However, the accurate classification of these images is still a big challenge. Previous studies demonstrate the effectiveness of combination of spectral data and spatial information for better classification of hyperspectral images. In this article, this approach is followed to propose a novel three-step spectral–spatial method for classification of hyperspectral images. In the first step, Gabor filters are applied for texture feature extraction. In the second step, spectral and texture features are separately classified by a probabilistic Support Vector Machine (SVM) pixel-wise classifier to estimate per-pixel probability. Therefore, two probabilities are obtained for each pixel of the image. In the third step, the total probability is calculated by a linear combination of the previous probabilities on which a control parameter determines the efficacy of each one. As a result, one pixel is assigned to one class which has the highest total probability. This method is performed in multivariate analysis framework (MAF) on which one pixel is represented by a d-dimensional vector, d is the number of spectral or texture features, and in functional data analysis (FDA) on which one pixel is considered as a continuous function. The proposed method is evaluated with different training samples on two hyperspectral data. The combination parameter is experimentally obtained for each hyperspectral data set as well as for each training samples. This parameter adjusts the efficacy of the spectral versus texture information in various areas such as forest, agricultural or urban area to get the best classification accuracy. Experimental results show high performance of the proposed method for hyperspectral image classification. In addition, these results confirm that the proposed method achieves better results in FDA than in MAF. Comparison with some state-of-the-art spectral–spatial classification methods demonstrates that the proposed method can significantly improve classification accuracies.     

结合孪生网络和像素配对的高光谱图像异常检测

目的 高光谱遥感中,通常利用像素的光谱特征来区分背景地物和异常目标,即通过二者之间的光谱差异来寻找图像中的异常像元。但传统的异常检测算法并未有效挖掘光谱的深层特征,高光谱图像中丰富的光谱信息没有被充分利用。针对这一问题,本文提出结合孪生神经网络和像素配对策略的高光谱图像异常检测方法,利用深度学习技术提取高光谱图像的深层非线性特征,提高异常检测精度。方法 采用像素配对的思想构建训练样本,与原始数据集相比,配对得到的新数据集数量呈指数增长,从而满足深度网络对数据集数量的需求。搭建含有特征提取模块和特征处理模块的孪生网络模型,其中,特征处理模块中的卷积层可以专注于提取像素对之间的差异特征,随后利用新的训练像素对数据集进行训练,并将训练好的分类模型固定参数,迁移至检测过程。用滑动双窗口策略对测试集进行配对处理,将测试像素对数据集送入网络模型,得到每个像素相较于周围背景像素的差异性分数,从而识别测试场景中的异常地物。结果 在异常检测的实验结果中,本文提出的孪生网络模型在San Diego数据集的两幅场景和ABU-Airport数据集的一幅场景上,得到的AUC （area under the curve）值分别为0.993 51、0.981 21和0.984 38,在3个测试集上的表现较传统方法和基于卷积神经网络的异常检测算法具有明显优势。结论 本文方法可以提取输入像素对的深层光谱特征,并根据其特征的差异性,让网络学习到二者的区分度,从而更好地赋予待测像素相对于周围背景的异常分数。本文方法相对于卷积神经网络的异常检测方法可以有效地降低虚警,与传统方法相比能够更加明显地突出异常目标,提高了检测率,同时也具有较强的鲁棒性。     

非监督正交子空间投影的高光谱混合像元自动分解

利用混合像元线性分解技术处理高光谱影像，以获取研究区域中同一像元的不同组份是遥感应用的主要目的之一。近年来，研究者们发展了一种正交子空间投影技术(0SP)，用来探测感兴趣目标，进一步可以用来分解混合像元，然而应用这种方法分解混合像元的缺陷是需要有研究区域的先验信息，这就制约了它在这方面的应用。为此针对这种不足，提出一种非监督的正交子空间投影(UOSP)技术，用来自动获取影像端元光谱，同时进行混合像元分解。并用成像光谱数据(PHI)实例测试了这个方法，结果表明该方法自动获取的端元比较合理，且分解混合像元精度较高。     

A spatial–spectral kernel-based approach for the classification of remote-sensing images

Classification of remotely sensed images with very high spatial resolution is investigated. The proposed method deals with the joint use of the spatial and the spectral information provided by the remote-sensing images. A definition of an adaptive neighborhood system is considered. Based on morphological area filtering, the spatial information associated with each pixel is modeled as the set of connected pixels with an identical gray value (flat zone) to which the pixel belongs: The pixel's neighborhood is characterized by the vector median value of the corresponding flat zone. The spectral information is the original pixel's value, be it a scalar or a vector value. Using kernel methods, the spatial and spectral information are jointly used for the classification through a support vector machine formulation. Experiments on hyperspectral and panchromatic images are presented and show a significant increase in classification accuracies for peri-urban area: For instance, with the first data set, the overall accuracy is increased from 80% with a conventional support vectors machines classifier to 86% with the proposed approach. Comparisons with other contextual methods show that the method is competitive.     

Spectral–spatial co-clustering of hyperspectral image data based on bipartite graph

The high dimensionality of hyperspectral images are usually coupled with limited data available, which degenerates the performances of clustering techniques based only on pixel spectral. To improve the performances of clustering, incorporation of spectral and spatial is needed. As an attempt in this direction, in this paper, we propose an unsupervised co-clustering framework to address both the pixel spectral and spatial constraints, in which the relationship among pixels is formulated using an undirected bipartite graph. The optimal partitions are obtained by spectral clustering on the bipartite graph. Experiments on four hyperspectral data sets are performed to evaluate the effectiveness of the proposed framework. Results also show our method achieves similar or better performance when compared to the other clustering methods.     

基于改进的胶囊网络模型的高光谱图像分类方法

针对卷积神经网络无法有效提取高光谱图像光谱与空间特征以及识别特征之间的空间位置问题,提出一种基于胶囊网络的改进神经网络模型.采用1×1卷积核对高光谱图像块进行降维处理;利用双通道卷积神经网络提取降维图像的初级特征,进而在PrimaryCaps层将初级特征信息封装为胶囊向量;通过DigitCaps层计算胶囊向量的模长来判...