Subpixel hyperspectral target detection using local spectral and spatial information

In this paper, we review and compare the state-of-the-art target detection algorithms. We introduce a new target detection workflow incorporating a Minimum Noise Fraction (MNF) transform before target detection. Applying a MNF transform was found to improve the detection results in general, especially with the Orthogonal Subspace Projection detector. In this paper, we propose a new algorithm - Mixture Tuned Target-Constrained Interference-Minimized Filter (MTTCIMF). MTTCIMF uses the MNF transformed image as the input and combines the mixture tuned technique with the TCIMF target detector. By adding an additional infeasibility band, mixture tuned techniques improve the detection results with a reduced number of false alarms. A HyMap data set with ground truth is used in the comparative study. Quantitative and visual evaluation of different algorithms is given. A new quantitative metric is proposed to evaluate the visibility of targets in the detection results. Keywords: target detection, mixture tuned matched filter (MTMF), mixture tuned target-constrained interferenceminimized filter (MTTCIMF), minimum noise fraction (MNF), adaptive coherence estimator (ACE), orthogonal subspace projection (OSP), constrained energy minimization (CEM), target visibility

This paper considers a multivariate quickest detection problem with false data injection (FDI) attacks in internet of things (IoT) systems. We derive a sequential generalized likelihood ratio test (GLRT) for zero-mean Gaussian FDI attacks. Exploiting the fact that covariance matrices are positive, we propose strategies to detect positive semi-definite matrix additions rather than arbitrary changes in the covariance matrix. The distribution of the GLRT is only known asymptotically whereas quickest detectors deal with short sequences, thereby leading to loss of performance. Therefore, we use a finite-sample correction to reduce the false alarm rate. Further, we provide a numerical approach to estimate the threshold sequences, which are analytically intractable to compute. We also compare the average detection delay of the proposed detector for constant and varying threshold sequences. Simulations showed that the proposed detector outperforms the standard sequential GLRT detector.

Summary The primary purpose of this paper is to propose empirical measures of the structural differences between two communities of plants or animals composed of the same species. Structure is defined to consist of; 1) the species in the community, 2) the pattern of interactions as represented by the covariance or correlation matrix of successive observations on each species, and 3) the mean abundances of each species in each of the two communities. Statistical tests are proposed for testing whether the covariance matrices and the vectors of mean densities for each community are equal and empirical measures of the differences between the covariance matrices and mean vectors are proposed. Given unequal covariance or correlation matrices the factor analysis model is used to derive empirical measures of the degree to which each variable of the ecosystem is responsible for the observed defferences in the pattern of interactions in each community. These tests and measures were applied to data gathered by Hunter (1966) on the abundances of six species of Drosophila censused monthly over a period of approximately two and a half years in two adjacent, but different habitats near Bogota, colombia. The two covariance matrices were significantly different indicating different patterns of interactions in the two Drosophila communities.

This letter introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals, ${\bf R}$ , is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal, ${\bf R}_{0}$ , is assumed to be equal to $\lambda{\bf R}$ , i.e., ${\bf R}_{0}=\lambda{\bf R}$ . On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor $\lambda$ between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE.

This paper proposes a practical way to solve the Uplink-Downlink Covariance Conversion (UDCC) problem in a frequency Division Duplex (FDD) communication system. The UDCC problem consists in the estimation of the Downlink (DL) spatial covariance matrix from the prior knowledge of the Uplink (UL) spatial covariance matrix without the need of a feedback transmission from the User Equipment (UE) to the Base Station (BS). Estimating the DL sample spatial covariance matrix is unfeasible in current massive Multiple-Input Multiple-Output (MIMO) deployments in frequency selective or fast fading channels due to the required large training overhead. Our method is based on the application of sparse filtering ideas to the estimation of a quantized version of the so-called Angular Power Spectrum (APS), being the common factor between the UL and DL spatial channel covariance matrices.

Detecting and identifying targets or objects that are present inhyperspectral ground images are of great interest. Applicationsinclude land and environmental monitoring, mining, military, civilsearch-and-rescue operations, and so on. We propose and analyze anextremely simple and efficient idea for template matching based on$l_1$ minimization. The designed algorithm can be applied inhyperspectral classification and target detection. Synthetic imagedata and real hyperspectral image (HSI) data are used to assess theperformance, with comparisons to other approaches, e.g. spectralangle map (SAM), adaptive coherence estimator (ACE),generalized-likelihood ratio test (GLRT) and matched filter. Wedemonstrate that this algorithm achieves excellent results with bothhigh speed and accuracy by using Bregman iteration.

In most hyperspectral target detection applications, targets are usually small and require both spatial as well as spectral detection. Hyperspectral imaging facilitates target detection (TD) applications greatly, however, due to large spectral content, hyperspectral data requires dimensionality reduction (DR) which also leads to loss of target information both at full pixel and subpixel level. Literature reports many DR and TD algorithms in practice. Several studies have focussed on assessing the loss of target information in DR, however, not much work seems to have been done to assess loss of target information in full pixel and subpixel TD in hyperspectral data with and without DR. This paper seeks to study various combinations of DR techniques combined with full pixel and subpixel TD algorithms. The results indicate that in the case of full pixel targets, both DR and TD contribute to the loss of target information, however, there is more loss of target information in the case when DR precedes TD in comparison to a case where TD is applied without DR. In the case of subpixel TD, however, there appears to be loss of subpixel target information in the case where TD alone is performed in comparison to a case where DR precedes TD.

Our goals in hyperspectral point target detection have been to develop a methodology for algorithm comparison and to advance point target detection algorithms through the fundamental understanding of spatial and spectral statistics. In this paper, we review our methodology as well as present new metrics. We demonstrate improved performance by making better estimates of the covariance matrix. We have found that the use of covariance matrices of statistical stationary segments in the matched-filter algorithm improves the receiver operating characteristic curves; proper segment selection for each pixel should be based on its neighboring pixels. We develop a new type of local covariance matrix, which can be implemented in principal-component space and which also shows improved performance based on our metrics. Finally, methods of fusing the segmentation approach with the local covariance matrix dramatically improve performance at low false-alarm rates while maintaining performance at higher false-alarm rates.

The realtime implementation is discussed for several detection and classification techniques: Orthogonal Subspace Projection (OSP), Filter Vector Algorithm (FVA), Generalized Likelihood Ratio Test (GLRT), RX algorithm, Constrained Energy Minimization (CEM), Target Constrained Interference Minimization Filter (TCIMF), and Constrained Linear Discriminant Analysis (CLDA). Two data dimensionality limitations are met in realtime processing. One is the number of classes to be classified cannot be larger than data dimensionality, i.e., the number of spectral bands (for some techniques), and the other is the number of independent pixel vectors used for processing must be larger than the number of bands for a data sample correlation or covariance matrix with full rank (for some techniques). In this paper, we present methods to take care of these two limitations: the former is solved by generating artificial band images to expand the data dimensionality, while the latter is solved by using a positive definite correlation matrix as initial matrix. Experiments using hyperspectral data and multispectral data demonstrate the effectiveness of these methods.

In this study, the applications of target detection algorithms such as Constrained Energy Minimization (CEM), Orthogonal Subspace Projection (OSP) and Adaptive Coherence Estimator (ACE) to shortwave infrared bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data was investigated to extract geological information for alteration mineral mapping in poorly exposed lithologies in inaccessible domains. The Oscar II coast area north-eastern Graham Land, Antarctic Peninsula (AP) was selected in this study to conduct a satellite-based remote sensing mapping technique. It is an inaccessible region due to the remoteness of many rock exposures and the necessity to travel over sever mountainous and glacier-cover terrains for geological field mapping and sample collection. Fractional abundance of alteration minerals such as muscovite, kaolinite, illite, montmorillonite, epidote, chlorite and biotite were identified in alteration zones using CEM, OSP and ACE algorithms in poorly mapped and unmapped zones at district scale for the Oscar II coast area. The results of this investigation demonstrated the applicability of ASTER shortwave infrared spectral data for lithological and alteration mineral mapping in poorly exposed lithologies and inaccessible regions, particularly using the image processing algorithms that are capable to detect sub-pixel targets in the remotely sensed images, where no prior information is available.

Constrained energy minimization (CEM) is one of the most widely used target detection methods due to its simplicity and effectiveness. From a mathematical perspective, the detection result can be regarded as the summation of bands weighted by the corresponding components of the CEM operator. Based on this point of view, we propose a supervised band selection idea for CEM target detection. Three different band selection methods for CEM (namely, BSC1, BSC2 and BSC3) are presented and compared. Some comparative experiments based on real hyperspectral data are conducted to evaluate the performance of these methods (the presented and other two methods). The results show that BSC3 is the most effective band selection method. The CEM detection result is quite acceptable using only a few of the bands selected by BSC3. BSC1 and BSC2 are also more accurate than other compared band selection methods when the number of selected bands is relatively large (>18 in our experiments).

Leaf maturation from initiation to senescence is a phenological event of plants that results from the influences of temperature and water availability on physiological activities during a life cycle. Detection of newly grown leaves (NGL) is therefore useful for the diagnosis of tree growth, tree stress, and even climatic change. This paper applies Constrained Energy Minimization (CEM), which is a hyperspectral target detection technique to spot grown leaves in a UAV multispectral image. According to the proportion of NGL in different regions, this paper proposes three innovative CEM based detectors: Subset CEM, Sliding Window-based CEM (SW CEM), and Adaptive Sliding Window-based CEM (AWS CEM). AWS CEM can especially adjust the window size according to the proportion of NGL around the current pixel. The results show that AWS CEM improves the accuracy of NGL detection and also reduces the false alarm rate. In addition, the results of the supervised target detection depend on the appropriate signature. In this case, we propose the Optimal Signature Generation Process (OSGP) to extract the optimal signature. The experimental results illustrate that OSGP can effectively improve the stability and the detection rate.

Stealth target becomes serious growing threats to the military radar, especially with the recently sub-Nyquist scattering wave deception jammer (SN-SWDJ) which has not countering solution yet, because of the fast sampling rate. This paper engages in improving detection of stealth target (F-117A) under SN-SWDJ effect by using stratospheric balloon-borne bistatic radar system with higher aspect vision. The potential problems of proposed scheme come from heterogeneity caused by sudden gusts of wind, and on the other hand, stealth target is always included in the array data due to the coupling of SN-SWDJ with target motion. To tackle these problems, we introduce an optimal suppression technique with a novel combination of knowledge-aided space-time adaptive processing (KA-STAP) and fast time robust adaptive beamforming (FTRAB) algorithms. FTRAB is applied to remove stealth target components from the array data, as the initial guess of KA-STAP covariance matrix. However, our proposed scheme has a higher complexity of the covariance matrix inversion. To reduce this complexity, we propose the linear affine combination of the inverse covariance matrices' estimation instead of the covariance matrix itself. Simulations demonstrate that the proposed scheme gives much higher location accuracy and reduces complexity comparing with other algorithms.

Estimating large covariance and precision (inverse covariance) matrices has become increasingly important in high dimensional statistics because of its wide applications. The estimation problem is challenging not only theoretically due to the constraint of its positive definiteness, but also computationally because of the curse of dimensionality. Many types of estimators have been proposed such as thresholding under the sparsity assumption of the target matrix, banding and tapering the sample covariance matrix. However, these estimators are not always guaranteed to be positive-definite, especially, for finite samples, and the sparsity assumption is rather restrictive. We propose a novel two-stage adaptive method based on the Cholesky decomposition of a general covariance matrix. By banding the precision matrix in the first stage and adapting the estimates to the second stage estimation, we develop a computationally efficient and statistically accurate method for estimating high dimensional precision matrices. We demonstrate the finite-sample performance of the proposed method by simulations from autoregressive, moving average, and long-range dependent processes. We illustrate its wide applicability by analyzing financial data such S&P 500 index and IBM stock returns, and electric power consumption of individual households. The theoretical properties of the proposed method are also investigated within a large class of covariance matrices.

Target spectrum based feature selection (TSFS): A new method based on chain coding for target detection problems