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.     

Fast anomaly detection in hyperspectral images with RX method on heterogeneous clusters

Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. One of the most widely used and successful algorithms for anomaly detection in hyperspectral images is the RX algorithm. Despite its wide acceptance and high computational complexity when applied to real hyperspectral scenes, few approaches have been developed for parallel implementation of this algorithm. In this paper, we evaluate the suitability of using a hybrid parallel implementation with a high-dimensional hyperspectral scene. A general strategy to automatically map parallel hybrid anomaly detection algorithms for hyperspectral image analysis has been developed. Parallel RX has been tested on an heterogeneous cluster using this routine. The considered approach is quantitatively evaluated using hyperspectral data collected by the NASA’s Airborne Visible Infra-Red Imaging Spectrometer system over the World Trade Center in New York, 5 days after the terrorist attacks. The numerical effectiveness of the algorithms is evaluated by means of their capacity to automatically detect the thermal hot spot of fires (anomalies). The speedups achieved show that a cluster of multi-core nodes can highly accelerate the RX algorithm.     

FPGA implementation of collaborative representation algorithm for real-time hyperspectral target detection

Hyperspectral image contains various wavelength channels and the corresponding imagery processing requires a computation platform with high performance. Target and anomaly detection on hyperspectral image has been concerned because of its practicality in many real-time detection fields while wider applicability is limited by the computing condition and low processing speed. The field programmable gate arrays (FPGAs) offer the possibility of on-board hyperspectral data processing with high speed, low-power consumption, reconfigurability and radiation tolerance. In this paper, we develop a novel FPGA-based technique for efficient real-time target detection algorithm in hyperspectral images. The collaborative representation is an efficient target detection (CRD) algorithm in hyperspectral imagery, which is directly based on the concept that the target pixels can be approximately represented by its spectral signatures, while the other cannot. To achieve high processing speed on FPGAs platform, the CRD algorithm reduces the dimensionality of hyperspectral image first. The Sherman–Morrison formula is utilized to calculate the matrix inversion to reduce the complexity of overall CRD algorithm. The achieved results demonstrate that the proposed system may obtains shorter processing time of the CRD algorithm than that on 3.40 GHz CPU.     

Near lossless compression of hyperspectral images based on distributed source coding

Effective compression technique of on-board hyperspectral images has been an active topic in the field of hyperspectral remote sensintg.In order to solve the effective compression of on-board hyperspectral images,a new distributed near lossless compression algorithm based on multilevel coset codes is proposed.Due to the diverse importance of each band,a new adaptive rate allocation algorithm is proposed,which allocates rational rate for each band according to the size of weight factor defined for hyperspectral images subject to the target rate constraints.Multiband prediction is introduced for Slepian-Wolf lossless coding and an optimal quantization algorithm is presented under the correct reconstruction of Slepian-Wolf decoder,which minimizes the distortion of reconstructed hyperspectral images under the target rate.Then Slepian-Wolf encoder exploits the correlation of the quantized values to generate the final bit streams.Experimental results show that the proposed algorithm has both higher compression efficiency and lower encoder complexity than several existing classical algorithms.     

基于最小噪声分离变换的高光谱异常检测方法研究

RX算法和核RX算法能很好地分离目标和背景,是较为广泛使用的异常检测算法,但是高光谱图像数据量大且存在冗余信息和噪声,直接进行RX及核RX异常探测运算量大且容易受噪声影响.针对此问题,提出一种基于最小噪声分离变换的高光谱图像异常检测方法,首先采用残差分析法估计噪声协方差矩阵以改进最小噪声分离变换,然后利用改进后的最小噪声分离变换来有效地降低高光谱图像数据的维数并分离出噪声,最后对低维降噪后的数据进行RX及核RX异常检测,避免了随机噪声对RX及核RX异常检测结果的影响并提高了异常检测率.对真实的AVIRIS数据测试表明,该算法优于传统的相应的RX、核RX异常检测算法.     

Comparison of spectral and spatial windows for local anomaly detection in hyperspectral imagery

A new way of implementing two local anomaly detectors in a hyperspectral image is presented in this study. Generally, most local anomaly detector implementations are carried out on the spatial windows of images, because the local area of the image scene is more suitable for a single statistical model than for global data. These detectors are applied by using linear projections. However, these detectors are quite improper if the hyperspectral dataset is adopted as the nonlinear manifolds in spectral space. As multivariate data, the hyperspectral image datasets can be considered to be low-dimensional manifolds embedded in the high-dimensional spectral space. In real environments, the nonlinear spectral mixture occurs more frequently, and these manifolds could be nonlinear. In this case, traditional local anomaly detectors are based on linear projections and cannot distinguish weak anomalies from background data. In this article, local linear manifold learning concepts have been adopted, and anomaly detection algorithms have used spectral space windows with respect to the linear projection. Output performance is determined by comparison between the proposed detectors and the classic spatial local detectors accompanied by the hyperspectral remote-sensing images. The result demonstrates that the effectiveness of the proposed algorithms is promising to improve detection of weak anomalies and to decrease false alarms.     

Adaptive linear minimum mean square error restoration: influence on hyperspectral detection strategy

In this paper, we consider the problem of multichannel restoration. Current multichannel least squares restoration filters assume the separability of the signal covariance, which describes the between‐channel and within‐channel relationships. We propose a new solution for a multichannel restoration scheme, the Adaptive Linear Minimum Mean Square Error (ALMMSE), based on a local signal model, without the hypothesis of spectral and spatial separability. The proposed filter is developed to be used as a preprocessing step for detection in hyperspectral imagery. Tests on real data show that the proposed filter enables us to enhance detection performance in target detection and anomaly detection applications with the well‐known hyperspectral imagery detection algorithms AMF and RX. The comparison with detection results, after classical restoration methods, shows the superiority of the proposed approach for hyperspectral images.     

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.     

基于鱼群算法的高光谱图像稀疏分解研究

研究高光谱图像的稀疏分解问题,能够降低高光谱图像的数据量,便于后续处理,但传统正交匹配追踪算法的计算复杂度高、不能满足实时处理要求。针对上述问题,提出了一种利用人工鱼群算法实现高光谱图像稀疏分解的算法。算法采用人工鱼群自上而下的寻优模式,通过鱼群中各个体的局部寻优,实现全局最优值在群体中凸显出来的目的,以人工鱼群的更新过程对正交匹配追踪算法的匹配过程进行改进,完成稀疏分解。实验结果表明,与正交匹配追踪算法相比,所提算法计算复杂度低,计算效率提高15倍,且能够提高重构图像的峰值信噪比,充分说明改进算法能够满足实时性要求,更有利于实现高光谱图像的稀疏分解。     

A suite of parallel algorithms for efficient band selection from hyperspectral images

The analysis of hyperspectral images is usually very heavy from the computational point-of-view, due to their high dimensionality. In order to avoid this problem, band selection (BS) has been widely used to reduce the dimensionality before the analysis. The aim is to extract a subset of the original bands of the hyperspectral image, preserving most of the information contained in the original data. The BS technique can be performed by prioritizing the bands on the basis of a score, assigned by specific criteria; in this case, BS turns out in the so-called band prioritization (BP). This paper focuses on BP algorithms based on the following parameters: signal-to-noise ratio, kurtosis, entropy, information divergence, variance and linearly constrained minimum variance. In particular, an optimized C serial version has been developed for each algorithm from which two parallel versions have been derived using OpenMP and NVIDIA’s compute unified device architecture. The former is designed for a multi-core CPU, while the latter is designed for a many-core graphics processing unit. For each version of these algorithms, several tests have been performed on a large database containing both synthetic and real hyperspectral images. In this way, scientists can integrate the proposed suite of efficient BP algorithms into existing frameworks, choosing the most suitable technique for their specific applications.     

基于改进RX算法的高光谱异常检测

针对RX算法中局部背景协方差矩阵估计的局限性,提出一种改进的RX (I-RX)异常检测算法。基于奇异值分解(SVD),将高光谱图像投影到背景的正交子空间上,获得仅包含噪声和异常的残留图像。在此基础上,通过计算各样本的空间秩深度将残留图像划分为噪声背景和潜在异常两个样本集,利用噪声背景集估计整幅图像的背景协方差矩阵,并将潜在异常集作为测试样本进行异常检测。对模拟数据和真实高光谱数据进行了实验仿真,ROC曲线表明,在相同的虚警概率下,I-RX算法的检测概率相较于RX平均提高了2倍左右。     

Rapid real-time generation of super-resolution hyperspectral images through compressive sensing and GPU

Recently, compressive sensing (CS) has offered a new framework whereby a signal can be recovered from a small number of noisy non-adaptive samples. This is now an active area of research in many image-processing applications, especially super-resolution. CS algorithms are widely known to be computationally expensive. This paper studies a real time super-resolution reconstruction method based on the compressive sampling matching pursuit (CoSaMP) algorithm for hyperspectral images. CoSaMP is an iterative compressive sensing method based on the orthogonal matching pursuit (OMP). Multi-spectral images record enormous volumes of data that are required in practical modern remote-sensing applications. A proposed implementation based on the graphical processing unit (GPU) has been developed for CoSaMP using computed unified device architecture (CUDA) and the cuBLAS library. The CoSaMP algorithm is divided into interdependent parts with respect to complexity and potential for parallelization. The proposed implementation is evaluated in terms of reconstruction error for different state-of-the-art super-resolution methods. Various experiments were conducted using real hyperspectral images collected by Earth Observing-1 (EO-1), and experimental results demonstrate the speeding up of the proposed GPU implementation and compare it to the sequential CPU implementation and state-of-the-art techniques. The speeding up of the GPU-based implementation is up to approximately 70 times faster than the corresponding optimized CPU.     

一种改进的超宽条带噪声消除算法

针对高光谱遥感图像中的超宽条带噪声干扰现象,在深入研究高光谱图像特点和条带噪声产生机理的基础上,提出了一种新的基于最小序列值、小波变换和矩匹配相结合的滤波算法（OWM算法）。该算法主要包括灰度对比度处理、最小序列值处理、小波变换系数归零处理和矩匹配处理等四个步骤。用实际的高光谱图像进行了一系列的验证比较实验,获得了好的实验效果。实验结果表明OWM算法不仅能够有效滤除高光谱图像中的超宽条带噪声,而且还具有较好的普适性。     

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.     

A projection pursuit algorithm for anomaly detection in hyperspectral imagery

The main goal of this paper is to propose an innovative technique for anomaly detection in hyperspectral imageries. This technique allows anomalies to be identified whose signatures are spectrally distinct from their surroundings, without any a priori knowledge of the target spectral signature. It is based on an one-dimensional projection pursuit with the Legendre index as the measure of interest. The index optimization is performed with a simulated annealing over a simplex in order to bypass local optima which could be sub-optimal in certain cases. It is argued that the proposed technique could be considered as seeking a projection to depart from the normal distribution, and unfolding the outliers as a consequence. The algorithm is tested with AHS and HYDICE hyperspectral imageries, where the results show the benefits of the approach in detecting a great variety of objects whose spectral signatures have sufficient deviation from the background. The technique proves to be automatic in the sense that there is no need for parameter tuning, giving meaningful results in all cases. Even objects of sub-pixel size, which cannot be made out by the human naked eye in the original image, can be detected as anomalies. Furthermore, a comparison between the proposed approach and the popular RX technique is given. The former outperforms the latter demonstrating its ability to reduce the proportion of false alarms.     

Multi-scale superpixel spectral–spatial classification of hyperspectral images

A novel multi-scale superpixel-based spectral–spatial classification (MS-SSC) approach is proposed for hyperspectral images in this study. Superpixels are considered as the basic processing units for spectral–spatial-based classification. The use of multiple scales allows the capturing of local spatial structures of various sizes. The proposed technique consists of three steps. In the first step, hierarchical superpixel segmentations are performed from fine to coarse scales for the original hyperspectral image and the spectral information of each superpixel is used for classification at each scale. In the second step, each single scale superpixel-based classification is improved by combining with the segmentations at a higher level. Finally, the multi-scale classification is achieved via decision fusion. Experimental results are presented for two hyperspectral images and compared with recently advanced pixel-wise and pixel-based spectral–spatial classification approaches. The experiments demonstrate that the proposed method works effectively on the homogeneous regions and is also able to preserve the small local spatial structures in the image.     

整合超像元分割和峰值密度的高光谱图像聚类

目的 传统图像聚类算法多利用像元的光谱信息,较少考虑图像的空间信息,容易受到噪声干扰。针对该问题,提出一种整合超像元分割（SLIC）和峰值密度（DP）的高光谱图像聚类算法。方法 首先,利用超像元分割技术对高光谱图像进行分割并提取超像元光谱特征;然后,根据提取的超像元光谱特征,计算其峰值密度信息,搜索超像元光谱簇,构建像元与类别间的隶属度关系。最后,利用高光谱模拟数据以及两组真实高光谱图像评价算法的鲁棒性和精度。结果 在不同信噪比的模拟数据中,SLIC-DP算法在调整芮氏指标（ARI）最优的条件下,较K-means和SLIC-Kmeans的方差降低61.86%和41.61%,体现优越的鲁棒性。在高光谱数据集Salinas-A和Indian Pines中,SLIC-DP算法的ARI为0.777 1和0.325 7,较K-Means和SLIC-KMeans聚类算法分别增长10.71%,5.01%与78.86%,25.27%。结论 本文算法抗噪声能力强,充分利用空间信息与光谱信息,有效提升高光谱图像聚类精度。经验证,能满足高光谱图像信息提取和分析的要求,可进一步推广和研究。     

A semi-supervised spatially aware wrapper method for hyperspectral band selection

Band selection is widely used to identify relevant bands for land-cover classification of hyperspectral images. The combination of spectral and spatial information can improve the classification performance of hyperspectral images dramatically. Similarly, the fusion of spectral–spatial information should also improve the performance of band selection. In this article, two semi-supervised wrapper-based spectral–spatial band selection algorithms are proposed. The local spatial smoothness of hyperspectral imagery is used to improve the performance of band selection when limited labelled samples available. With superpixel segmentation, the first algorithm uses the statistical characteristics of classification map to predict the classification quality of all samples. Based on the Markov random field model, the second algorithm incorporates the spatial information by the minimization of spectral–spatial energy function. Four widely used real hyperspectral data sets are used to demonstrate the effectiveness of the proposed methods, when compared to cross-validation-based wrapper method, the accuracy is improved by 2% for different data sets.     

GPU implementation of RX detection using spectral derivative features

Hyperspectral image (HSI), which can record abundance information of a pixel, has shown huge potential on many applications such as image classification, target and anomaly detection and so on. Nowadays, anomaly detection has attracted more attention because there is no limitation of spectral library. A standard approach for anomaly detection is the method developed by Reed and Xiaoli, called RX algorithm. However, the data volume is getting bigger with the developing of imaging technology. A problem that ensues is the rapid increase of computation complexity and this will lead a time-consumed application. In addition, there will be noise in HSI with the influence of illumination and atmospheric. In this paper, we realize an implementation of RX algorithm on NVIDIA GeForce 1060 GPU with the utilization of derivative features. On one hand, the GPU parallel implementation reach the purpose of real-time processing and it also eliminates the storage burden of on-board processing. On the other hand, the derivative features have better performance on salient features detection and noise restraint. Thus, it can further promote the detection performance of RXD. In our experiments, three real HSI datasets were tested to verify the effect of GPU parallel implementation. The experiment results had indicated that the utilization of derivative features can promote the detection performance. Compared with serial computation, the parallel implementation achieves a great reduction on processing time.     

Maximized subspace model for hyperspectral anomaly detection

An important application in remote sensing using hyperspectral imaging system is the detection of anomalies in a large background in real-time. A basic anomaly detector for hyperspectral imagery that performs reasonaly well is the RX detector. In practice, the subspace RX (SSRX) detector which deletes the clutter subspace has been known to perform better than the RX detector. In this paper an anomaly detector that can do better than the SSRX detector without having to delete the clutter subspace is developed. The anomaly detector partials out the effect of the clutter subspace by predicting the background using a linear combination of the clutter subspace. The Mahalanobis distance of the resulting residual is defined as the anomaly detector. The coefficients of the linear combination are chosen to maximize a criterion based on squared correlation. The experimental results are obtained by implementing the anomaly detector as a global anomaly detector in unsupervised mode with background statistics computed from hyperspectral data cubes with wavelengths in the visible and near-infrared range. The results show that the anomaly detector has a better performance than the SSRX detector. In conclusion, the anomaly detector that is based on partialling out can achieve better performance than the conventional anomaly detectors.