NOVEL HYPERSPECTRAL ANOMALY DETECTION METHODS BASED ON UNSUPERVISED NEAREST REGULARIZED SUBSPACE

Hyperspectral imagery systems have the ability to collect 3D digital images with rich spatial and spectral information. Anomaly detection is one of the interesting applications over last two decades in hyperspectral imagery. In this paper, we propose Collaborative Representation-Based with Outlier Removal Anomaly Detector (CRBORAD) method for HSI Anomaly Detection. We use both spectral and spatial information for detecting anomalies instead of using only spectral information that was introduced in our previous work. The proposed detector can adaptively estimate the background by its adjacent pixels within a sliding dual window. Before estimating background pixels, we remove outlier pixels that are significantly different from majority of pixels. It leads us to precise background approximation and better accuracy for detecting anomalies in subsequent stages. The residual image is constituted by subtracting the predicted background from the original HSI, and anomalies can be determined in the residual image, finally. Kernel extension of the proposed approach is also presented. We implemented the proposed algorithms on San Diego airport hyperspectral data. CRBORAD results are illustrated using receiver-operating-characteristic (ROC) curves, Area Under Curve (AUC) values and intuitive images. Comparing the results of the current study with four popular and previous methods shows that CRBORAD provides us an accurate method for detecting anomalies.

In this paper, collaborative representation is proposed for anomaly detection in hyperspectral imagery. The algorithm is directly based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. The representation is assumed to be the linear combination of neighboring pixels, and the collaboration of representation is reinforced by l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm minimization of the representation weight vector. To adjust the contribution of each neighboring pixel, a distance-weighted regularization matrix is included in the optimization problem, which has a simple and closed-form solution. By imposing the sum-to-one constraint to the weight vector, the stability of the solution can be enhanced. The major advantage of the proposed algorithm is the capability of adaptively modeling the background even when anomalous pixels are involved. A kernel extension of the proposed approach is also studied. Experimental results indicate that our proposed detector may outperform the traditional detection methods such as the classic Reed-Xiaoli (RX) algorithm, the kernel RX algorithm, and the state-of-the-art robust principal component analysis based and sparse-representation-based anomaly detectors, with low computational cost.

The hyperspectral/spatial detection of edges (HySPADE) algorithm, originally published in 2004 [1], has been modified and applied to a wider diversity of hyperspectral imagery (HSI) data. As originally described in [1], HySPADE operates by converting the naturally two-dimensional edge detection process based on traditional image analysis methods into a series of one-dimensional edge detections based on spectral angle. The HySPADE algorithm: i) utilizes spectral signature information to identify edges; ii) requires only the spectral information of the HSI scene data and does not require a spectral library nor spectral matching against a library; iii) facilitates simultaneous use of all spectral information; iv) does not require endmember or training data selection; v) generates multiple, independent data points for statistical analysis of detected edges; vi) is robust in the presence of noise; and vii) may be applied to radiance, reflectance, and emissivity data--though it is applied to radiance and reflectance spectra (and their principal components transformation) in this report. HySPADE has recently been modified to use Euclidean distance values as an alternative to spectral angle. It has also been modified to use an N x N-pixel sliding window in contrast to the 2004 version which operated only on spatial subset image chips. HySPADE results are compared to those obtained using traditional (Roberts and Sobel) edge-detection methods. Spectral angle and Euclidean distance HySPADE results are superior to those obtained using the traditional edge detection methods; the best results are obtained by applying HySPADE to the first few, information-containing bands of principal components transformed data (both radiance and reflectance). However, in practice, both the Euclidean distance and spectral angle versions of HySPADE should be applied and their results compared. HySPADE results are shown; extensions of the HySPADE concept are discussed as are applications for HySPADE in HSI analysis and exploitation.

Anomaly detection in Hyperspectral Imagery (HSI) has received considerable attention because of its potential application in several areas. Numerous anomaly detection algorithms for HSI have been proposed in the literature; however, due to the use of different datasets in previous studies, an extensive performance comparison of these algorithms is missing. In this paper, an overview of the current state of research in hyperspectral anomaly detection is presented by broadly dividing all the previously proposed algorithms into eight different categories. In addition, this paper presents the most comprehensive comparative analysis to-date in hyperspectral anomaly detection by evaluating 22 algorithms on 17 different publicly available datasets. Results indicate that attribute and edge-preserving filtering-based detection (AED), local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW) and local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector (LSUNRSORAD) perform better as indicated by the mean and median values of area under the receiver operating characteristic (ROC) curves. Finally, this paper studies the effect of various dimensionality reduction techniques on anomaly detection. Results indicate that reducing the number of components to around 20 improves the performance; however, any further decrease deteriorates the performance.

With the rapid development of sensor technology, the anomaly detection of multi-source time series data becomes more and more important. Traditional anomaly detection methods deal with the temporal and spatial information in the data independently, and fail to make full use of the potential of spatio-temporal information. To address this issue, this paper proposes a novel integration method that combines sensor embeddings and temporal representation networks, effectively exploiting spatio-temporal dynamics. In addition, the graph neural network is introduced to skillfully simulate the complexity of multi-source heterogeneous data. By applying a dual loss function—consisting of a reconstruction loss and a prediction loss—we further improve the accuracy of anomaly detection. This strategy not only promotes the ability to learn normal behavior patterns from historical data, but also significantly improves the predictive ability of the model, making anomaly detection more accurate. Experimental results on four multi-source sensor datasets show that our proposed method performs better than the existing models. In addition, our approach enhances the ability to interpret anomaly detection by analyzing the sensors associated with the detected anomalies.

Traditional target detection (TD) methods for hyperspectral imagery (HSI) suffer from background interference. In this paper, we propose a novel preprocessing method based on tensor principal component analysis (TPCA) to separate the background and target apart. In our approach, HSI is decomposed into the sum of the principal component (PC) part and the residual part, and TD is performed on the latter. TPCA takes spatial and spectral information into account jointly, and treats spatial and spectral information differently, which is in line with HSI physical meanings. Experiments on both synthetic and real data indicate that our TPCA-based method outperforms other feature extraction preprocessing methods in terms of TD results.

With the increasing of hyperspectral datasets, multi-temporal hyperspectral change detection has gradually attracted re-searcher's attention. Most of traditional change detection methods only consider spectral information, but ignore importance of spatial structure information, which leads to low detection accuracy. In this work, a novel patch tensor-based change detection method (PTCD) is proposed for hyperspectral imagery to make full use of spatial structure information. Firstly, the tensor decomposition and reconstruction strategies are used to eliminate influence of various factors in multi-temporal dataset. Meanwhile, patch-based strategy is adopted to incorporate the non-overlapping local similar property into the proposed method to exploit spatial structural information. Finally, a specially designed detector is adopted to further improve the detection accuracy. Experiments conducted on two real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.

Performance Analysis of Spatial Color Information for Object Detection Using Background Subtraction

To manage wetlands, resource managers require information on the size, shape, condition, and spatial distribution of vegetation types at both the community and species levels. Remote sensing methods currently used for generating maps of wetland ecosystems are largely based on spectral transformations of image data, preferably using high spatial resolution data. However, airborne hyperspectral imagery contains not only high spectral content but also considerable spatial information. To date, use of the spatial information in such imagery, particularly to determine biodiversity, has not been fully exploited. In this study, spatial heterogeneity in the reflectance values of hyperspectral imagery recorded over an inland wetland complex in South Dumfries Township, Ontario, Canada, is determined on a per-pixel basis by measuring the degree of local spatial association (local Moran’s Ii) within a 3 × 3 neighbourhood of pixels. These new spatio-spectral Moran’s Ii bands are then classified into heterogeneity categories using a modified version of the spectral angle mapper algorithm. Results show that the extraction of spatial information from hyperspectral data contributes to wetland management initiatives by providing quantitative information on the spatial arrangement of measured biological elements. The use of Moran’s Ii, a local measure of spatial association, also provides a statistical basis on which to map heterogeneity characteristics of elements within a wetland environment and to determine how these characteristics change over time.

Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy in hyperspectral imagery (HSI) classification. In this article, we propose a novel HSI feature extraction based on joint adaptive structure density (JASD), which can make full use of the texture feature of feature level and effectively utilize the spatial structure feature of pixel level. Specifically, the proposed JASD method considers two weak assumptions when exploiting the structure information contained in the HSI: 1) close pixels in spectral space have the same label and 2) all the pixels in a subregion belong to the same class. The JASD method consists of the following steps. First, a principal component analysis method is applied to the original high-dimensional HSI to obtain low-dimensional features in order to promote the separability of pixels between different classes and improve the execution efficiency of the proposed method. Then, the local density peak-assisted $k$ nearest neighbor idea is introduced into oversegmentation-based regions to define the structural spatial information of the HSI and overcome the aforementioned weak assumptions. Meanwhile, recursive filtering in the transform domain is applied to the above low-dimensional HSI to preserve global edge details. Next, the global edge details and the local spatial structure feature are fused based on the stacking method. Finally, the composite hyperspectral feature is fed into the supervised classifier to obtain the classification results. Experimental results on three widely used real HSI data sets demonstrated that the proposed JASD method is superior to several comparative classification methods in terms of classification accuracy and computational burden.

A novel regional energy minimization model is proposed in this paper, which aims to find the optimal clustering planes for respective objects in the image domain. By using the distances from the pixels to the center planes and spatial location information, the model assigns the pixels to the appropriate categories. A soft membership function is introduced to estimate the score which describes the possibility that the pixel falls into the category. Further, the spatial information is employed to amend the membership function so as to enhance the noise robustness of the model. The parameters of the center planes are updated through the energy minimization, and constrained by the prior values as well. The proposed model has been conducted on the synthetic images and real images, quantitatively and qualitatively, to demonstrate its performance.

Spatial modeling of the schistosomiasis mansoni in Minas Gerais State, Brazil using spatial regression

Hyperspectral anomaly detection, which is aimed at locating anomaly, has received widespread attention. In this article, a new anomaly detector, named local spatial constraint and total variation (LSC-TV), is proposed for hyperspectral imagery. In anomaly detection methods based on low-rank representation, background pixels are usually considered to have a global low-dimensional structure. However, the complex background distribution in hyperspectral images (HSIs) means that this global low-dimensional structure rarely occurs. In LSC-TV, the effective local spatial information is extracted by superpixel segmentation, and the regularization based on the F-norm is used to force the background within the same superpixel to show uniform spectral features. Moreover, each pixel is given a penalty based on the degree of anomaly determined during model iteration, while the anomaly is not considered by the background constraint. In addition, the background pixels in the neighborhood often show a high correlation, whereas the anomaly does not possess this feature. Nonisotropic TV is introduced into the proposed LSC model using the correlation of first-order neighborhoods to make it easier for anomalies to be separated. The proposed LSC-TV method and current state-of-the-art methods are tested on a set of simulated data and four sets of real data. The experimental results demonstrate that the proposed method is superior to the comparative method in terms of both color map detection and quantitative evaluation.

Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space

Local spatial properties based image interpolation scheme using SVMs