Low-Rank and Sparse Matrix Decomposition with Cluster Weighting for Hyperspectral Anomaly Detection

Abstract

Hyperspectral anomaly detection plays an important role in the field of remote sensing. It provides a way to distinguish interested targets from the background without any prior knowledge. The majority of pixels in the hyperspectral dataset belong to the background, and they can be well represented by several endmembers, so the background has a low-rank property. Anomalous targets usually account for a tiny part of the dataset, and they are considered to have a sparse property. Recently, the low-rank and sparse matrix decomposition (LRaSMD) technique has drawn great attention as a method for solving anomaly detection problems. In this letter, a new anomaly detection method based on LRaSMD and cluster weighting is proposed. We concentrate on the sparse part, which contains most of anomaly information, and calculate the initial anomaly matrix based on this part. To suppress background regions and discriminate anomalies from the background more distinctly, a weighting strategy in terms of the clustering result is used, and then the anomaly matrix is updated. The judgement of anomalies is made according to the responses on the matrix. Our proposed method considers the characteristics of anomalies from the spectral dimension and the spatial distribution simultaneously. Experiments on three hyperspectral datasets demonstrate the outstanding performance of the proposed method.

1. Introduction

Hyperspectral imagery (HSI) is important in remote sensing applications, because it provides abundant spatial and spectral information for Earth observation. Target/anomaly detection is a significant research topic in hyperspectral remote sensing [1,2,3,4,5]. In a target detection task, the prior information of interested objects’ spectral signatures is necessary. So, it can be carried out when target signatures are known. In situations where target signatures are unavailable, we need to utilize anomaly detection algorithms to search for interested regions. In recent decades, anomaly detection has attracted much attention in the HSI processing field [6,7,8].

Anomalous targets in HSI consist of a few pixels or subpixels whose spectral signatures are extremely different from the homogenous background. In general, anomaly pixels occupy only a tiny part of the image, and the main task of an anomaly detector is to distinguish them from the background. There are plenty of anomaly detection algorithms that have been proposed in the past; the Reed-Xiaoli detector (RX) [9] is one of the most typical methods therein. It simply models the background as a multivariate Gaussian distribution and calculates the square root of the Mahalanobis distance between the pixel under test and the background. The RX detector is computationally efficient and has good detection performance on the datasets which satisfy the assumed background distribution model. Furthermore, a series of improved algorithms based on the RX detector have been proposed and widely used, such as the kernel RX (KRX) [10], the cluster kernel RX (CKRX) [11], the subspace RX (SSRX) [12], the weighted RX [13], the cluster-based anomaly detector (CBAD) [14], and the dual window-based eigen separation transform detector (DWEST) [15]. Apart from the aforementioned methods, some other concepts and techniques have also been introduced in anomaly detection. For instance, the blocked adaptive computationally efficient outlier nominator detector (BACON) [16] tries to purify the background model through robust statistics obtained from subsets of the dataset, which can improve the detection performance. The collaborative representation-based detector (CRD) [17] assumes that background pixels can be well represented by the linear combination of its spatial neighbors, while anomaly pixels cannot. In Reference [18], anomaly detection is conducted by adopting a Gaussian Mixture Model (GMM) to describe the statistics of the background, and the number of Gaussian components is automatically assessed by a mixture learning technique. In Reference [19], a metric is developed to account for anisotropic heavy tails in covariance-whitened data. It can be used to improve the detection results of other existing methods. Moreover, some low-rank-based anomaly detection methods have also been researched in depth. In Reference [20], the spectral unmixing algorithm is first applied to obtain the abundance vectors. Afterwards, a dictionary is built by using the mean-shift clustering of the abundance vectors. Finally, the low-rank decomposition based on the dictionary is proposed to extract anomaly objects. In Reference [21], a source component-based anomaly detection approach is designed, which uses the blind source component separation and identifies the components that are anomalous (or salient) compared to other components. It converts the anomaly detection to a low-rank matrix decomposition problem with the minimum volume constraint for the multi-modular background and sparsity constraint for the anomaly pixels. Recently, the low-rank and sparse matrix decomposition (LRaSMD)-based anomaly detection method [22] was proposed, which calculates the Euclidean distance between each pixel and the mean vector on the sparse part. Moreover, the LRaSMD-based Mahalanobis distance method (LSMAD) [23] utilizes the low-rank component and calculates the Mahalanobis distance for the test pixel. These LRaSMD-based methods all yield superior detection performance compared with other state-of-the-art detectors. However, they only consider the anomaly pixels’ spectral characteristics, but ignore their spatial distribution characteristics, which may influence the detection performance to some extent.

A novel anomaly detection method, which combines LRaSMD and cluster weighting, is proposed in this letter. For an original HSI dataset, the LRaSMD technique is first applied. Then we focus on the sparse matrix and calculate the ${\ell }_2$-norm of each pixel on this component. The result can be denoted as the initial anomaly matrix. It is considered that some information about the background with a sparse property may be contained in the sparse part. Therefore, these background pixels have high responses on the initial anomaly matrix, which can weaken the detection performance. So, in this method, a cluster weighting strategy is designed. Based on the result of $k$-means clustering, homogenous domains larger than the threshold are suppressed through a weighting function. Finally, an updated anomaly matrix with more reasonable anomaly values is obtained, which is deemed as the final detection result. The main advantages of our proposed method include: (1) making full use of the sparse property of anomalous targets to detect them from the perspective of spectral difference; (2) taking into account the spatial distribution characteristic of anomalous targets, which makes the detection result more accurate.

The remainder of the letter is organized as follows. Section 2 briefly introduces the LRaSMD model, and then describes our proposed method in detail. Section 3 gives the experimental results and parameter analysis. The discussion part is presented in Section 4. Finally, conclusions are drawn in Section 5.

2. LRaSMD with Cluster Weighting for Anomaly Detection

2.1. LRaSMD Model for HSI Dataset

Assume an HSI dataset can be represented by a matrix $\mathrm{H}\in {ℝ}^{N\times B}$, where $N$ and $B$ denote the number of image pixels and spectral bands, respectively. It can be decomposed to a background matrix $B$ and an anomaly matrix $A$, which is defined as:

$$H=B+A,$$

(1)

However, in real-world situations, the observed data is always contaminated by noise, which can be modeled as independent and identically distributed Gaussian noise. Therefore, Equation (1) is modified as:

$$H=B+A+N,$$

(2)

where $N$ is the noise matrix. For one specific HSI dataset, the background spectral signatures can be denoted as the linear combinations of several endmembers. So, matrix $B$ is considered to lie in a low-dimension subspace, which has a low-rank property. Moreover, anomalies in the dataset always have a low probability of occurrence. Thus, matrix $A$ can be assumed to have a sparse property. Therefore, the HSI dataset $H$ can be decomposed into Equation (2) by using the LRaSMD technique [22,23,24,25,26]. Figure 1 shows the decomposed data cubes of different parts.

In recent years, some fast and effective methods have been developed to solve the problem of LRaSMD, such as the randomized approximate matrix decomposition (RAMD) [27] and the robust principal component analysis (RPCA) [28]. In this letter, the Go Decomposition (GoDec) algorithm [29] is applied. It is a fast version for “low-rank + sparse” decomposition, which uses the bilateral random projections (BRP) to replace the singular-value decomposition (SVD) in the algorithm. The GoDec algorithm works through minimizing the decomposition error in Equation (2):

$$\underset{B,A}{\min }{\Vert H-B-A\Vert }_F^{2}s.t.\mathrm{rank}\left(B\right)\le r,\mathrm{card}\left(A\right)\le kN,$$

(3)

where ${\Vert •\Vert }_F^2$ is the Frobenius norm, and $r$ and $k$ represent the upper bound of the rank of $B$ and the cardinality (i.e., the sparsity level) of $A$, respectively. The optimization problem in Equation (3) can be solved by alternatively solving two subproblems as follows:

$$\{\begin{array}{l}{B}_t=\arg \underset{\mathrm{rank}\left(B\right)\le r}{\min }{\Vert H-B-A_{t-1}\Vert }_F^2\\ A_t=\arg \underset{\mathrm{card}\left(A\right)\le kN}{\min }{\Vert H-B_{t-1}-A\Vert }_F^2\end{array},$$

(4)

where $t$ is the iteration time. In order to reduce the time cost, the BRP-based low-rank approximation is implemented to solve Equation (4). Then $B_t$ is updated as:

$$B_t=Y_1{\left(R_2^T{Y}_1\right)}^{-1}{Y}_2^T,$$

(5)

where $Y_1=H{R}_1$ and $Y_2=H^T{R}_2$, wherein $R_1\in {ℝ}^{B\times r}$ and $R_2\in {ℝ}^{N\times r}$ are random matrices.

In each iteration, $A_t$ is updated through the entry-wise hard thresholding of $H-B_{t-1}$ as follows:

$$\begin{array}{l}{A}_t=P_{\Omega }\left(H-B_{t-1}\right),\Omega :\left|{\left(H-B_{t-1}\right)}_{i,j\in \Omega }\right|\ne 0\\ \mathrm{and}\ge \left|{\left(H-B_{t-1}\right)}_{i,j\in \Omega }\right|,|\Omega |\le kN.\end{array}$$

(6)

where $\Omega$ represents the nonzero subset of the first $kN$ largest entries of $\left|H-B_{t-1}\right|$, and $P_{\Omega }(•)$ refers to a matrix’s projection to an entry set $\Omega$.

2.2. Anomaly Detection Based on LRaSMD and Cluster Weighting

According to the derivation aforementioned, an HSI dataset can be decomposed into one low-rank background matrix, one sparse anomaly matrix, and one noise matrix. The low-rank background matrix contains most of the background information, and the sparse anomaly matrix extracts the anomaly part effectively. Besides, the distribution of different components in the image also contains substantial spatial information. Based on this idea, some related works have already been conducted in the fields of HSI classification [30] and soil detection in multispectral images [31]. In this letter, we try to detect anomalies from HSI by taking advantage of the sparse matrix and considering the spatial distribution characteristics of the anomaly/background simultaneously. This approach is elaborated as follows.

For the tested HSI datase t $\mathrm{H}\in {ℝ}^{N\times B}$, the GoDec [29] algorithm is executed, then three decomposed components in Equation (2) are obtained. Assume the sparse anomaly matrix $A$ can be denoted as $A=\left[A_1,A_2,\dots ,A_N\right]$, whose columns ${\left\{A_i\right\}}_{i=1,\dots ,N}$ are spectral vectors. For each pixel, the initial anomaly value $v_i$ is calculated by using its ${\ell }_2$-norm:

$$v_i={\Vert A_i(:)\Vert }_2\left(1\le i\le N\right),$$

(7)

Equation (7) reflects the response of each pixel on the sparse matrix. The larger value of $v_i$ means the corresponding pixel is more likely to belong to an anomaly, and vice versa. However, in real cases, some background regions with small areas also have large initial anomaly values because of their sparse property. To help understand this phenomenon, an example is given in Figure 2. From the surf map of the initial anomaly matrix, it can be observed that some background pixels have higher values than anomalies, which can weaken the detection performance. In fact, pixels belonging to the same background material always have similar spectral signatures. Therefore, if we can locate background regions accurately, then the suppression strategy for background pixels can be applied and these false alarms will be mitigated. Thus, in order to make our approach more powerful, the cluster weighting approach is introduced.

The $k$-means clustering method [32] is used on the original dataset. The purpose is to generate a series of classes with spectral similarity. We assume that there are a total of $K$ classes (centroids) in the dataset, which is determined manually, mainly based on the complexity of the background components in the scene. If the HSI dataset consists of many types of background materials, $K$ should be set relatively large. Otherwise, it should be set as a small value. For an arbitrary pixel $x_i\left(1\le i\le N\right)$, if its class is $c\left(1\le c\le K\right)$, then the number of pixels belonging to $c$ in its eight-connected domain is counted. It can be considered that these pixels have high spectral similarity with $x_i$. Furthermore, all of these pixels are connected in the spatial dimension. Thus, if the number of these pixels is larger than a threshold $T$, then it is reasonable to determine the corresponding eight-connected domain as a background region. A schematic illustration is shown in Figure 3. Obviously, $T$ is related to the class number $K$. If $K$ is relatively large, then the dataset will be clustered more fragmentarily, so $T$ should be set to a small value. If $K$ is small, the cluster map will be more integral, then $T$ should be set relatively large. Hence, we can conclude theoretically that $T$ and $K$ have an inverse relationship, and the product of them is a constant. In this letter, it is named the background constant $B$, which is predefined in our method. Then, the threshold $T$ can be obtained as follows:

$$T=\frac{B}{K},$$

(8)

There are mainly two aspects that need to be taken into consideration to determine $B$: (1) the number of eight-connected domains in the cluster map, which should be set relatively small if there exists a large number of eight-connected domains; and (2) the approximate sizes of anomalous targets in the scene. We can set $B$ as a large value if the targets are large, otherwise the value can be set to a small value.

Assume the cluster map can be segmented into $L$ non-overlapping eight-connected domains, denoted as $D=\left\{D_1,D_2,\dots ,D_L\right\}$. Each domain can be classified into the background domain set $D^B$ or the anomaly domain set $D^A$ based on the following rule:

$$\{\begin{array}{cc}{D}_i\subseteq D^B,& \mathrm{if}num\left(D_i\right)\ge T,1\le i\le L\\ D_i\subseteq D^A,& \mathrm{if}num\left(D_i\right)<T,1\le i\le L\end{array},$$

(9)

where $num(•)$ represents the pixel number of one certain domain. Suppose there are a total of $L_B$ domains in $D^B$, denoted as $D^B=\left\{D_1^B,D_2^B,\dots ,D_{L_B}^B\right\}$, and the pixel number of each domain consists of a set $NB=\left\{num\left(D_1^B\right),num\left(D_2^B\right),\dots ,num\left(D_{L_B}^B\right)\right\}$. Pixels locating in $D^B$ can be deemed as background pixels with a high possibility. Then, an exponential weighting function is designed for these pixels:

$$f\left(x_i\right)=\exp \left(-\frac{num\left(D_l^B\right)}{\min \left(NB\right)}\right),x_i\in D_l^B,1\le i\le N,1\le l\le L_B,$$

(10)

where $\min (•)$ represents the minimal number in the set. The range of $f(•)$ is $\left(0,e^{-1}\right]$, so every pixel in $D^B$ can be assigned an appropriate weight according to the domain to which they belong, and pixels belonging to a larger domain will have a smaller weight. Finally, the anomaly value for each pixel $x_i$ is updated as follows:

$${v^{\prime }}_i=\{\begin{array}{cc}{v}_i,& \mathrm{if}{x}_i\in D^A\\ f\left(x_i\right)\cdot v_i,& \mathrm{if}{x}_i\in D^B\end{array},$$

(11)

Equation (11) distinctly suppresses the response of background components owing to the cluster weighting item, which can make the detection result better.

The proposed method is called LRaSMD with cluster weighting for anomaly detection (LSwCW). Its main procedure is summarized as Algorithm 1, and the corresponding flowchart is given in Figure 4.

Algorithm 1 LRaSMD with cluster weighting for anomaly detection

Input (1) The original HSI dataset $H$; (2) the upper bound of the rank $r$; (3) the sparsity level $k$; (4) the number of clustering centroids $K$; (5) the background constant $B$. Solve the optimization problem in Equation (3) using the GoDec algorithm [29]. Calculate the initial anomaly value $v_i$ for each pixel according to Equation (7). Use the $k$-means clustering on the original dataset, and obtain the eight-connected domains of background according to Equations (8) and (9). Update the initial anomaly value $v_i$ to ${v^{\prime }}_i$ for each pixel according to Equations (10) and (11).    Output Anomaly detection map.

3. Experimental Results and Analysis

3.1. Dataset Description

Three real HSI datasets are used to evaluate the effectiveness of the proposed method. The first was acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor from the San Diego Airport, San Diego, CA, USA, with the size of 100 × 100 pixels. There are a total of 224 spectral bands ranging from 370–2510 nm. After eliminating the low-signal-to-noise ratio (SNR) and water vapor absorption bands, 189 bands are retained. Three airplanes in the scene, which contain 58 pixels, are considered as anomalous targets. The false color image and the ground-truth map are shown in Figure 5. The second is a Hyperspectral Digital Imagery Collection Experiment (HYDICE) dataset, which covers an urban area with a spectral resolution of 10 nm. It has 210 spectral bands in total, and 162 bands are used after removing the low-SNR and water vapor absorption bands. The size of the scene is 79 × 100 pixels; the false color image and the corresponding ground-truth map are shown in Figure 6. The third is the MUUFL Gulfport dataset, which is selected from the free hyperspectral datasets available to the remote sensing community [33,34,35,36,37]. It was collected in November 2010 over the University of Southern Mississippi Gulf Park Campus, located in Long Beach, Mississippi. The hyperspectral sensor has 72 spectral bands. Its wavelength range is 375–1050 nm, with a bandwidth of 10 nm. After removing the noise bands, there are 64 bands are used in the dataset, and the spatial size is 325 × 220. The RGB image of the dataset is shown in Figure 7a. Four cloth panels in the scene (see Figure 7c) are denoted as anomalous targets in our experiments. Their ground-truth map is given in Figure 7b.

3.2. Detection Performance

Several existing state-of-the-art anomaly detectors, including RX [9], kernel RX (KRX) [10], DWEST [15], CRD [17], and LRaSMD [22], are compared with our proposed LSwCW method to evaluate its performance. The experimental platform is a computer with an Intel Core i5-5200U central processing unit at 2.20 GHz and 12 GB random access memory. It is noteworthy that in all comparative experiments, the parameters of all algorithms are set as the optimal. A detailed parameter analysis of LSwCW will be described in the next subsection.

The color detection maps of compared algorithms for the AVIRIS dataset are represented in Figure 8. It can be seen that the anomalous targets are more distinguishable in the detection map of LSwCW when compared with the other results. Meanwhile, the homogeneous background can be well suppressed by LSwCW. For the HYDICE dataset, the detection result of each algorithm is shown in Figure 9. We can see that LSwCW suppresses most of background regions, and the true anomalous targets have high response values. Besides, RX, KRX, and CRD also provide good detection results, but a few anomaly pixels in these maps have low values, which influences their performances. For the MUUFL Gulfport dataset, the color detection maps of different algorithms are shown in Figure 10. The result of LSwCW reveals that anomalous targets can be effectively extracted by our proposed method, while the majority of the background can be suppressed. From other subfigures, we can see RX, CRD, and LRaSMD also obtain superior detection results, but in comparison, LSwCW is the best.

In order to further investigate the detection performance quantitatively, the receiver operating characteristic (ROC) curves with point-wise confidence intervals [38] are first provided. They are drawn on the log10 coordinate, and the point-wise confidence bounds are estimated based on bootstrap. Furthermore, the normalized background-anomaly separation maps are also utilized. For one specific detector, values of its detection map are first normalized to 0–1. Then two boxes are generated; the cyan box represents the value distribution of all background pixels, and the magenta box stands for the value distribution of all anomaly pixels. The central mark of the box indicates the median, while the bottom and top edges indicate the 25th and 75th percentiles, respectively. In addition, the whiskers extend to the most extreme data points not considered outliers. One detector has good separation performance if there exists an obvious gap between two boxes.

For the AVIRIS dataset, the ROC curves with point-wise confidence intervals are demonstrated in Figure 11a. As we can see, LSwCW obtains a remarkable ROC curve located at the upper-left corner of the figure, and its point-wise confidence bounds are always higher than the other detectors when the True Positive Rate (TPR) varies from 0 to 1. After it, LRaSMD and KRX also have better ROC curves with point-wise confidence intervals than the other algorithms. In addition, the normalized background-anomaly separation map of each algorithm is shown in Figure 11b. It can be seen that LSwCW suppresses the background information into a small value range, and discriminates anomalies from the background obviously. This proves that LSwCW has a superior separation performance compared to the other detection methods. For the HYDICE dataset, the quantitative evaluations are presented in Figure 12. From Figure 12a, we can see that the ROC curve of LSwCW is the best, and the corresponding confidence intervals are also higher than the others. According to the normalized background-anomaly separation map in Figure 12b, it can be observed that LSwCW has the best separation performance among the compared algorithms. The quantitative evaluation results of the MUUFL Gulfport dataset are demonstrated in Figure 13. The results of the ROC curves with point-wise confidence intervals and normalized background-anomaly separation map further illustrate that LSwCW has the most advanced detection performance compared with the other approaches. Furthermore, the area under the curve (AUC) values of each algorithm for the three datasets are reported in Figure 14a–c, respectively. From this figure, we can see that LSwCW has the highest AUC values in all three datasets, thus exhibiting outstanding detection performance. Finally, the computational costs of all compared algorithms for three datasets are listed in

Table 1. Computational costs of different algorithms for the AVIRIS, HYDICE, and MUUFL Gulfport datasets.

Time (s)	RX	KRX	DWEST	CRD	LRaSMD	LSwCW
AVIRIS	0.24	1.74	57.74	87.94	0.78	3.41
HYDICE	0.38	1.35	46.32	68.46	0.45	2.25
MUUFL Gulfport	0.65	23.12	1292.32	541.05	2.03	17.17

. The statistics reveal that LSwCW has high computational efficiency.

3.3. Parameter Sensitivity Analysis

There are four important parameters in LSwCW: the upper bound of the rank $r$, the sparsity level $k$, the number of clustering centroids $K$, and the background constant $B$. Experiments in this subsection are implemented on the AVIRIS, HYDICE, and MUUFL Gulfport datasets. In addition, when we analyze the specific parameter(s), the others are set to the optimal.

The rank $r$ is related to the main background materials in the scene. Without the loss of generality, for the AVIRIS and HYDICE datasets, the range of $r$ is set as (1, 2, 3, 4, 5, 10, 15, 20, 30, 40); for the MUUFL Gulfport dataset, it is set as (10, 15, 20, 25, 30, 35, 40, 45, 50, 55). In addition, the sparsity level $k$ is determined empirically, and all three datasets are set as (0.1, 0.2, 0.3, 0.5, 0.7, 1). For each dataset, the various detection performances with different combinations of $r$ and $k$ are exhibited by a three-dimensional (3D) plot, whose z-axis represents the corresponding AUC value. As shown in Figure 15, for the AVIRIS dataset, the AUC values are higher than 0.99 with any combinations of $r$ and $k$, which reflects the robustness of LSwCW to the transformations of these two parameters. For the HYDICE dataset, the AUC values are relatively low when $r$ is less than 3 and $k$ is lower than 0.3 simultaneously. On other positions, it can achieve a high value over 0.99. This may be because the HYDICE dataset has complex background components, which should be described satisfactorily by more than three basis spectral vectors. Therefore, the rank $r$ should be relatively large. Moreover, some anomaly pixels may be mixed by background spectra with high proportions, so it is necessary to set $k$ in a relatively high value to detect them. For the MUUFL Gulfport dataset, the AUC values are high and stable with the change of $r$ and $k$, especially when the sparsity level is larger than 0.7. This is because the cloth panel on the lower left is quite similar to the background of grass, so $k$ should be enlarged to extract it effectively.

The parameter $K$ can be set based on the number of main background materials in the image. The background constant $B$ is selected empirically, and usually is an integer multiple of $K$. Considering the difference of the main background categories in the three datasets, for the AVIRIS dataset, $K$ is set from 5 to 10, and $B$ ranges from 150 to 300, with a step interval of 50. For the HYDICE dataset, the value range of $K$ is 10 to 15, while $B$ is 300 to 600, with a step interval of 100. For the MUUFL Gulfport dataset, $K$ is set from 15 to 20, and $B$ is set as (3000, 3500, 4000, 4500). The results are also demonstrated by using 3D plots, which are shown in Figure 16. In general, LSwCW is quite robust to the parameters $K$ and $B$. Only in one case, in which $K$ is large while $B$ is too small, is the detection performance relatively weak, because this situation can lead to a small value of threshold $T$, which may misclassify the anomaly cluster as background.

4. Discussion

In the basis of the experimental results and the relevant parameter analyses, it can be considered that our proposed method has preeminent performance in terms of various evaluation indicators. Firstly, some state-of-the-art anomaly detectors are chosen as comparison algorithms. They are RX, KRX, DWEST, CRD, and LRaSMD. Among them, the RX detector is a well-known benchmark anomaly detection algorithm. The KRX detector is a nonlinear version of RX, which has been proven to be a superior algorithm. The DWEST represents the type of algorithms that utilize the dual window strategy. The CRD is one of the most typical representation-based methods. Finally, the LRaSMD is selected as a comparison algorithm since it uses the same technique as our proposed method. The qualitative analyses of different algorithms for the three datasets are demonstrated by color detection maps, which are shown in Figure 8, Figure 9 and Figure 10. These three figures indicate intuitively the good detection results of LSwCW. Compared with the other methods, LSwCW can well suppress background pixels because of the cluster weighting strategy. Meanwhile, anomaly pixels can be extracted obviously owing to the procedure of low-rank and sparse matrix decomposition. The quantitative analyses are given through ROC curves with point-wise confidence intervals, AUC values, as well as normalized background-anomaly separation maps. The better detector has an ROC curve which is closer to the upper-left corner, and the point-wise confidence bounds are higher in the case of the equal False Positive Rate (FPR). In addition, the corresponding AUC value is larger. Moreover, its background box and the anomaly box have a larger gap in the normalized background-anomaly separation map. The quantitative comparison results are depicted in Figure 11, Figure 12, Figure 13 and Figure 14. According to the aforementioned evaluation standards, LSwCW has the best ROC curves with point-wise confidence intervals for the three datasets, and its AUC values are the largest. Furthermore, the normalized background-anomaly separation maps also confirm the best separation performance of LSwCW in all compared algorithms. We also measured the computational cost of each algorithm, which is reported in Table 1. It can be seen that LSwCW is not the fastest, but its time consumptions still lie in the acceptable range for real applications. We believe that the computational efficiency can be further improved through optimizing codes.

Determining suitable parameters is important to obtain good detection results. To judge the capability of one algorithm, it is necessary to analyze its sensitivity to the transformations of different parameters. In our experiments, this is evaluated by changing four essential parameters: (1) the upper bound of the rank $r$; (2) the sparsity level $k$; (3) the number of clustering centroids $K$; and (4) the background constant $B$. All parameters are set to large value ranges; then, we combine two parameters together, and obtain the 3D plots. They are illustrated in Figure 15 and Figure 16, respectively. It can be seen that LSwCW is quite robust with regard to the parameters’ transformations. Only in a few special cases are the AUC values reduced, and the reasons for these phenomena were explained in detail in Section 3.3.

Although our proposed method yields an outstanding detection performance, there still exist some aspects which can be further investigated. For instance, the empirical parameters may influence the detection performance if they are set outside reasonable ranges. In the future, we will focus on how to determine all parameters automatically and adaptively, and we believe this will further improve the practicality of this method.

5. Conclusions

Detecting anomalous targets precisely and reliably from HSIs is an important research issue in hyperspectral remote sensing. Considering anomalies’ low probability of occurrence, significant spectral difference from main background materials, and spatial distribution characteristics, a new anomaly detection approach named LSwCW is proposed in this letter. The GoDec algorithm is first applied to decompose the original dataset to the low-rank part, the sparse part, and the noise part. The initial anomaly matrix is then obtained by calculating the ${\ell }_2$-norm for each pixel on the sparse matrix. To suppress the responses of background pixels, especially background regions with a sparse property, a cluster weighting strategy is utilized. Finally, the anomaly matrix is updated, which is denoted as the detection result. LSwCW makes full use of the sparse property of anomalies and extracts them based on the spectral difference. Meanwhile, the spatial distribution characteristics of anomalies and the background are also taken into account, which can make the result more accurate. Experimental results on three real-world HSI datasets showed the effectiveness, robustness, and low computation time of the proposed method.