A real-time unsupervised background extraction-based target detection method for hyperspectral imagery

Target detection is an important technique in hyperspectral image analysis. The high dimensionality of hyperspectral data provides the possibility of deeply mining the information hiding in spectra, and many targets that cannot be visualized by inspection can be detected. But this also brings some problems such as unknown background interferences at the same time. In this way, extracting and taking advantage of the background information in the region of interest becomes a task of great significance. In this paper, we present an unsupervised background extraction-based target detection method, which is called UBETD for short. The proposed UBETD takes advantage of the method of endmember extraction in hyperspectral unmixing, another important technique that can extract representative material signatures from the images. These endmembers represent most of the image information, so they can be reasonably seen as the combination of targets and background signatures. Since the background information is known, algorithm like target-constrained interference-minimized filter could then be introduced to detect the targets while inhibiting the interferences. To meet the rapidly rising demand of real-time processing capabilities, the proposed algorithm is further simplified in computation and implemented on a FPGA board. Experiments with synthetic and real hyperspectral images have been conducted comparing with constrained energy minimization, adaptive coherence/cosine estimator and adaptive matched filter to evaluate the detection and computational performance of our proposed method. The results indicate that UBETD and its hardware implementation RT-UBETD can achieve better performance and are particularly prominent in inhibiting interferences in the background. On the other hand, the hardware implementation of RT-UBETD can complete the target detection processing in far less time than the data acquisition time of hyperspectral sensor like HyMap, which confirms strict real-time processing capability of the proposed system.     

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.     

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.     

A multi-scale target detection method for optical remote sensing images

Multimedia Tools and Applications - Faster RCNN is a region proposal based object detection approach. It integrates the region proposal stage and classification stage into a single pipeline, which...     

Statistical thresholding method for infrared images

Conventional statistical thresholding methods use class variance sum as criterions for threshold selection. These approaches neglect specific characteristic of practical images and fail to obtain satisfactory results when segmenting some images with similar statistical distributions in the object and background. To eliminate the limitation, a novel statistical criterion is defined by utilizing standard deviations of two thresholded classes, and the optimal threshold is determined by optimizing the criterion. The proposed method was compared with several classic thresholding counterparts on a variety of infrared images as well as general real-world ones, and the experimental results demonstrate its superiority.     

扫描型红外图像实时目标检测研究

针对扫描型红外图像数据量大、背景复杂、横纹杂波干扰大和目标图像信噪比低等特点,提出一种适用于360°全方位扫描型红外图像目标检测的实时处理算法。算法预处理部分消除横纹杂波,实现目标增强和背景抑制;然后采用两级特征提取,大幅降低图像的数据量;最后根据帧间图像灰度特征相关的思想,完成扫描型红外图像目标检测的功能。根据本文算法的特点,以DSP+FPGA为核心处理器设计了具有实时处理能力强,数据传输容量大等优点的图像处理板。实验结果表明,本文算法是有效、可行的,图像处理板稳定、可靠,能满足扫描型红外图像实时目标检测的需求。     

A new residual fusion classification method for hyperspectral images

In this paper, we propose a novel residual fusion classification method for hyperspectral image using spatial–spectral information, abbreviated as RFC-SS. The RFC-SS method first uses the Gabor texture features and the non-parametric weighted spectral features to describe the hyperspectral image from both aspects of spatial and spectral information. Then it applies the residual fusion method to save the useful information from different classification methods, which can greatly improve the classification performance. Finally, the test sample is assigned to the class that has the minimal fused residuals. The RFC-SS classification method is tested on two classical hyperspectral images (i.e. Indian Pines, Pavia University). The theoretical analysis and experimental results demonstrate that the RFC-SS classification method can achieve a better performance in terms of overall accuracy, average accuracy, and the Kappa coefficient when compared to the other classification methods.     

A novel method for speckle noise reduction and ship target detection in SAR images

It is very difficult to detect small targets when the scattering intensity of background clutter is as strong as the targets and the speckle noise is serious in synthetic aperture radar (SAR) images. Because the scattering of man-made objects lasts for a longer time than that of background clutter in azimuth matching scope, it is much easier for man-made objects to produce strong coherence than ground objects. As the essence of SAR imaging is coherent imaging, the contrast between targets and background clutter can be enhanced via coherent processing of SAR images. This paper proposes a novel method to reduce speckle noise for SAR images and to improve the detected ratio for SAR ship targets from the SAR imaging mechanism. This new method includes the coherence reduction speckle noise (CRSN) algorithm and the coherence constant false-alarm ratio (CCFAR) detection algorithm. Real SAR image data is used to test the presented algorithms and the experimental results verify that they are feasible and effective.     

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.     

Histogram thresholding for unsupervised change detection of remote sensing images

The change-detection problem can be viewed as an unsupervised classification problem with two classes corresponding to changed and unchanged areas. Image differencing is a widely used approach to change detection. It is based on the idea of generating a difference image that represents the modulus of the spectral change vectors associated with each pixel in the study area. To separate out the changed and unchanged classes in the difference image automatically, any unsupervised technique can be used. Thresholding is one of the cheapest techniques among them. However, in thresholding approaches, selection of the best threshold value is not a trivial task. In this work, several non-fuzzy and fuzzy histogram thresholding techniques are investigated and compared for the change-detection problem. Experimental results, carried out on different multitemporal remote sensing images (acquired before and after an event), are used to assess the effectiveness of each of the thresholding techniques. Among all the thresholding techniques investigated here, Liu's fuzzy entropy followed by Kapur's entropy are found to be the most robust techniques.     

高光谱图像目标检测研究进展

目标检测是高光谱遥感领域一个重要研究方向,其在矿物勘探和国防侦查等领域都有着广泛的应用。简明、系统地介绍了高光谱图像目标检测中的一些关键算法及其在实际应用中存在的问题,并对未来发展方向进行了展望。     

Kernel matched subspace detectors for hyperspectral target detection

In this paper, we present a kernel realization of a matched subspace detector (MSD) that is based on a subspace mixture model defined in a high-dimensional feature space associated with a kernel function. The linear subspace mixture model for the MSD is first reformulated in a high-dimensional feature space and then the corresponding expression for the generalized likelihood ratio test (GLRT) is obtained for this model. The subspace mixture model in the feature space and its corresponding GLRT expression are equivalent to a nonlinear subspace mixture model with a corresponding nonlinear GLRT expression in the original input space. In order to address the intractability of the GLRT in the feature space, we kernelize the GLRT expression using the kernel eigenvector representations as well as the kernel trick where dot products in the feature space are implicitly computed by kernels. The proposed kernel-based nonlinear detector, so-called kernel matched subspace detector (KMSD), is applied to several hyperspectral images to detect targets of interest. KMSD showed superior detection performance over the conventional MSD when tested on several synthetic data and real hyperspectral imagery.     

Complexity-aware algorithm architecture for real-time enhancement of local anomalies in hyperspectral images

Anomaly detection (AD) from remotely sensed multi-hyperspectral images is a powerful tool in many applications, such as strategic surveillance and search and rescue operations. In a typical operational scenario, an airborne hyperspectral sensor searches a wide area to identify regions that may contain potential targets. These regions typically cue higher spatial-resolution sensors to provide target recognition and identification. While this procedure is mostly automated, an on-board operator is generally assigned to examine in real time the AD output and select the regions of interest to be sent for cueing. Real-time enhancement of local anomalies in images of the over flown scene can be presented to the operator to facilitate the decision-making process. Within this framework, one of the ultimate research interests is undoubtedly the design of complexity-aware AD algorithm architectures capable of assuring real-time or nearly real-time in-flight processing and prompt decision making. Among the different AD algorithms developed, this work focuses on those AD algorithms aimed at detecting small rare objects that are anomalous with respect to their local background. One of such algorithms, called RX algorithm, is based on a local Gaussian assumption for background and locally estimates its parameters from each pixel local neighborhood. RX has been recognized to be the benchmark AD algorithm for detecting local anomalies in multi-hyperspectral images. RX decision rule has been employed to develop computationally efficient algorithms tested in real-time operating systems. These algorithms rely upon a recursive block-based parameter estimation procedure that makes their processing and, in turn, their detection performance differ from those of original RX. In this paper, a complexity-aware algorithm architecture fully adaptable to real-time processing is presented that allows the computational load to be reduced with respect to original RX, while strictly following its original formulation and thus assuring the same detection performance. An experimental study is presented that analyzes in detail the complexity reduction, in terms of number of elementary operations, offered by the proposed architecture with respect to original RX. A real hyperspectral image of a scene with deployed targets has been employed to perform a case-study analysis of the complexity reduction to be experienced in different operational scenarios. The real data are also adopted to illustrate a possible strategy for on-board line-by-line enhanced visualization of anomalies for decision support.     

A reconfigurable multi-mode implementation of hyperspectral target detection algorithms

Hyperspectral images obtained by imaging spectrometer contain a large data amount that requires techniques such as target detection for information extraction. The proposed multi-mode FPGA implementation combines matrix correlation and inversion matrix computations by using the Sherman-Morrison method to achieve real-time operation. The implementation supports Constrained Energy Minimization (CEM), Adjusted Spectral Matched Filter (ASMF) and modified Adaptive Cosine Estimator (ACE) detectors. The detection performance of the algorithms is evaluated using standard detection metrics. The proposed implementation has been realized on Zynq family SoCs and verified against the MATLAB reference software. The detection results for different fixed-point data types and target detection algorithms are reported. Finally, the proposed implementation is compared with state-of-the-art designs in terms of both throughput and resource utilization.     

Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery

Target detection is one of the most important applications of hyperspectral imagery in the field of both civilian and military. In this letter, we firstly propose a new spectral matching method for target detection in hyperspectral imagery, which utilizes a pre-whitening procedure and defines a regularized spectral angle between the spectra of the test sample and the targets. The regularized spectral angle, which possesses explicit geometric sense in multidimensional spectral vector space, indicates a measure to make the target detection more effective. Furthermore Kernel realization of the Angle-Regularized Spectral Matching (KAR-SM, based on kernel mapping) improves detection even more. To demonstrate the detection performance of the proposed method and its kernel version, experiments are conducted on real hyperspectral images. The experimental tests show that the proposed detector outperforms the conventional spectral matched filter and its kernel version.     

Fully constrained target fraction estimation from noise-reduced hyperspectral images

Estimating abundance fractions of materials in hyperspectral images is a problem often studied in remote sensing. It can be used in reconnaissance and surveillance applications. The major difficulty of fraction estimation in hyperspectral images is due to the fact that the sampling distance is generally larger than the size of the targets of interest. Under this circumstance, estimation should be carried out at the sub-pixel level. In a linear mixture model, the spectrum of a mixed pixel is represented as a linear combination of the endmember spectra present in the pixel area weighted by fractional area coverage. Accurate fraction estimation requires two constraints (SC) imposed on abundance fractions: the abundance sum-to-one constraint and abundance non-negativity constraint (NC). In this article, we present a new fully constrained least squares computational method to estimate abundance fractions. Another contribution of this article is that the estimation is, unlike many other proposed methods, performed on noise-reduced hyperspectral images instead of original images. Experiments using synthetic data and Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data demonstrate that this fully constrained estimation outperforms the unconstrained and partially constrained least squares methods and that noise reduction can considerably improve the capability of our approach when the noise intensity rises.     

Novel precision target detection with adaptive thresholding for dynamic image segmentation

A new adaptive thresholding algorithm concerning extraction of targets from the background in a given image sequence is proposed. The conventional histogram-based or fixed-value thresholdings are deficient in detecting targets due to the poor contrast between targets and the background, or to the change of illumination. This research solves the problems mentioned above by learning the characteristics of the background from the given images and determines the proper thresholds based on this information. Experiments show that the proposed algorithm is superior to the optimal layering algorithm in target detection and tracking. Received: 28 December 1999 / Accepted: 8 August 2000     

灰度图像二维Otsu折线阈值分割法

Otsu法是一个应用较为广泛的阈值分割方法。为实现图像较为精确的分割,充分考虑边界的影响,从二维线阈值分割替代传统的点阈值分割思想出发,提出了折线阈值型Otsu法。该方法以对边界信息的迭代分割的手段获得实际用于分割的二维折线阈值。仿真结果表明,该方法能够获得优于原始Otsu法的分割效果,特别适用于边缘丰富的图像分割,具有较好的分割普适性。