A novel anomaly detection method incorporating target information derived from hyperspectral imagery

Guo, Qiaozhen; Pu, Ruiliang; Gao, Lianru; Zhang, Fangfang

doi:10.1080/2150704x.2015.1101177

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…Urban Data, available at http://www.tec.army.mil/Hypercube, is one of the most widely used hyperspectral data set for target detection [45,46]. It was recorded by the Hyperspectral Digital Imagery Collection Experiment (HYDICE) in October 1995, whose location is an urban area at CA, USA.…”

Section: Urban Datamentioning

confidence: 99%

A Novel FPGA-Based Architecture for Fast Automatic Target Detection in Hyperspectral Images

Lei

et al. 2019

Remote Sensing

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Onboard target detection of hyperspectral imagery (HSI), considered as a significant remote sensing application, has gained increasing attention in the latest years. It usually requires processing huge volumes of HSI data in real-time under constraints of low computational complexity and high detection accuracy. Automatic target generation process based on an orthogonal subspace projector (ATGP-OSP) is a well-known automatic target detection algorithm, which is widely used owing to its competitive performance. However, ATGP-OSP has an issue to be deployed onboard in real-time target detection due to its iteratively calculating the inversion of growing matrices and increasing matrix multiplications. To resolve this dilemma, we propose a novel fast implementation of ATGP (Fast-ATGP) while maintaining target detection accuracy of ATGP-OSP. Fast-ATGP takes advantage of simple regular matrix add/multiply operations instead of increasingly complicated matrix inversions to update growing orthogonal projection operator matrices. Furthermore, the updated orthogonal projection operator matrix is replaced by a normalized vector to perform the inner-product operations with each pixel for finding a target per iteration. With these two major optimizations, the computational complexity of ATGP-OSP is substantially reduced. What is more, an FPGA-based implementation of the proposed Fast-ATGP using high-level synthesis (HLS) is developed. Specifically, an efficient architecture containing a bunch of pipelines being executed in parallel is further designed and evaluated on a Xilinx XC7VX690T FPGA. The experimental results demonstrate that our proposed FPGA-based Fast-ATGP is able to automatically detect multiple targets on a commonly used dataset (AVIRIS Cuprite Data) at a high-speed rate of 200 MHz with a significant speedup of nearly 34.3 times that of ATGP-OSP, while retaining nearly the same high detection accuracy. data analysis, it is rather rare and may be difficult to find because many of HSI-detected targets are relatively small and weak, such as anomalies [10]. Consequently, real-time target detection in HSI presents a great challenge and has become increasingly important.Currently, many algorithms have been developed for target detection in HSI, such as unsupervised fully constrained least squares (UFCLS) [11], automatic target-generation process based on an orthogonal subspace projector (ATGP-OSP) [12], and Reed-Xiaoli (RX) detector [13]. Among them, the performance of ATGP performs well in terms of detection accuracy and computational complexity according to the quantitative and comparative assessments [14,15]. However, despite the fact that automatic target detection has received increasing attention [16][17][18], two problems with real-time onboard applications need to be addressed. One is to continuously increase the scale of operations in the process of automatically iterating to update currently being found targets, which complicates the process of updating operator matrix. The other one is a large-scale matrix m...

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Section: Urban Datamentioning

confidence: 99%

A Novel FPGA-Based Architecture for Fast Automatic Target Detection in Hyperspectral Images

Lei

et al. 2019

Remote Sensing

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“…RSAD and BACON [54] are two novel anomaly detection methods with the aim of obtaining an accurate background estimation by taking away the potential anomalies. Since the proposed method utilizes some knowledge of anomalies, we also compare with the BACON-target [55] method which further takes the abnormal target information into consideration after applying the BACON method in order to reduce its false alarm rate. We compare with the global KRX method by mapping the original feature into the high dimensional feature space as well.…”

Section: Competitorsmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation

Yuan

Wang

2018

Remote Sensing

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Most hyperspectral anomaly detection methods directly utilize all the original spectra to recognize anomalies. However, the inherent characteristics of high spectral dimension and complex spectral correlation commonly make their detection performance unsatisfactory. Therefore, an effective feature extraction technique is necessary. To this end, this paper proposes a novel anomaly detection method via discriminative feature learning with multiple-dictionary sparse representation. Firstly, a new spectral feature selection framework based on sparse presentation is designed, which is closely guided by the anomaly detection task. Then, the representative spectra which can significantly enlarge anomaly's deviation from background are picked out by minimizing residues between background spectrum reconstruction error and anomaly spectrum recovery error. Finally, through comprehensively considering the virtues of different groups of representative features selected from multiple dictionaries, a global multiple-view detection strategy is presented to improve the detection accuracy. The proposed method is compared with ten state-of-the-art methods including LRX, SRD, CRD, LSMAD, RSAD, BACON, BACON-target, GRX, GKRX, and PCA-GRX on three real-world hyperspectral images. Corresponding to each competitor, it has the average detection performance improvement of about 9.9%,

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Real-time kernel collaborative representation-based anomaly detection for hyperspectral imagery

Zhao

Yao

et al. 2020

Infrared Physics & Technology

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A novel anomaly detection method incorporating target information derived from hyperspectral imagery

Cited by 6 publications

References 21 publications

A Novel FPGA-Based Architecture for Fast Automatic Target Detection in Hyperspectral Images

A Novel FPGA-Based Architecture for Fast Automatic Target Detection in Hyperspectral Images

Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation

Real-time kernel collaborative representation-based anomaly detection for hyperspectral imagery

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