A Hyperspectral Target Detection Framework With Subtraction Pixel Pair Features

Du, Jinming; Li, Zhiyong

doi:10.1109/access.2018.2865963

Cited by 35 publications

(7 citation statements)

References 25 publications

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“…Their spatial resolution is 3.5 m, and the spectral resolution is about 10 nm. As for the spectral information, considering that the original 224 bands with spectra ranging from 370 nm to 2510 nm contain some useless bands due to water absorption regions or low-SNR, we remove several bands and finally retain 189 bands in the experiment according to [4]. The size of AVIRIS1 is 60 × 60 and it contains 14 planes as anomalies.…”

Section: A Data Setsmentioning

confidence: 99%

“…Its high spectral resolution with hundreds of narrow and approximately continuous spectral bands can provide a strong guarantee for discriminating the subtle differences of surface substances [2]. Owing to this advantage, hyperspectral image (HSI) processing techniques have been widely applied in different research fields, such as hyperspectral target detection [3], [4], hyperspectral image classification [5], [6], band selection [7], [8], and hyperspectral unmixing [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm

Yuan

Wang

2019

IEEE Access

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Hyperspectral anomaly detection is a research hot spot in the field of remote sensing. It can distinguish abnormal targets from the scene just by utilizing the spectral differences and requiring no prior information. A series of anomaly detectors based on Reed-Xiaoli methods are very important and typical algorithms in this research area, which generally have the hypothesis about background subject to the Gaussian distribution. However, this assumption is inaccurate to describe a hyperspectral image with a complex scene in practice. Besides, due to the unavoidable existence of abnormal targets, background statistics will be affected which will reduce the detection performance. To address these problems, we propose a sparse dictionary learning method by using a capped norm to realize hyperspectral anomaly detection. Moreover, a new training data selection strategy based on clustering technique is also proposed to learn a more representative background dictionary. The main contributions are concluded in threefold: 1) neither making any assumptions on the background distribution nor computing the covariance matrix, the proposed method is more adaptive to all kinds of complex hyperspectral images in practice; 2) owing to the good qualities of the capped norm, the learned sparse background dictionary is resistant to the effect of anomalies and has stronger distinctiveness to anomalies from background; 3) without using the traditional sliding hollow window technique, the proposed method is more effective to detect different sizes of abnormal targets. The extensive experiments on four commonly used real-world hyperspectral images demonstrate the effectiveness of the proposed method and show its superiority over the benchmark methods.

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Section: A Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm

Yuan

Wang

2019

IEEE Access

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show abstract

“…Moreover, because of the share of parameters to tackle the curse of dimensionality [10] and the local receptive field to learn spatial information [11], CNNs show good performance in HSI band selection [12,13], feature extraction and classification [14]. There are many CNN models used as HSI classifiers, including the spectral CNN [15,16], the spatial CNN [17,18], and the spectral-spatial CNN [19,20]. With the advance of remote sensing technology, better classifiers are being developed to further improve the classification performance and the complexity of CNNs.…”

Section: Introductionmentioning

confidence: 99%

Continuous Particle Swarm Optimization-Based Deep Learning Architecture Search for Hyperspectral Image Classification

et al. 2021

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Deep convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI) classification. However, the most successful CNN architectures are handcrafted, which need professional knowledge and consume a very significant amount of time. To automatically design cell-based CNN architectures for HSI classification, we propose an efficient continuous evolutionary method, named CPSO-Net, which can dramatically accelerate optimal architecture generation by the optimization of weight-sharing parameters. First, a SuperNet with all candidate operations is maintained to share the parameters for all individuals and optimized by collecting the gradients of all individuals in the population. Second, a novel direct encoding strategy is devised to encode architectures into particles, which inherit the parameters from the SuperNet. Then, particle swarm optimization is used to search for the optimal deep architecture from the particle swarm. Furthermore, experiments with limited training samples based on four widely used biased and unbiased hyperspectral datasets showed that our proposed method achieves good performance comparable to the state-of-the-art HSI classification methods.

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“…Target detection (TD) is one of the most popular research fields of HSI that aims to discover the interesting targets and separate them from the complex background [3]. However, most TD algorithms need to obtain the prior information of desired targets and background before detection [4], [5]. The prior knowledge…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Graphical Connected Point Estimation and Multiple Support Vector Machines

et al. 2020

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Most hyperspectral anomaly detection algorithms are based on various hypothetical models justified by different methods. The closer to the real-world scene distribution a hypothetical model is, the better detection performance usually results, albeit at the expense of increased model complexity. There is also a challenge in most of the detection methods that anomalous components cannot be completely separated from the background due to the difference between hypothetical models and the real-world scene. To address this problem, a novel strategy based on graph theory and the multiple support vector machines technique is proposed in this paper. Firstly, a graphical connected point-based estimation is utilized as a preprocessing procedure to separate potential anomalies from robust backgrounds of the image. Second, without building a hypothetical model, the hyperspectral image is classified into several categories by a clustering method according to the different characteristics of materials. Consequently, for each category, the support vector machine is used to classify anomalous components and background components belonging to specific categories, and the result map of each classification is obtained. Finally, all maps corresponding to various categories are fused by an effective fusion strategy, and the detection result is generated by our method. In the subsequent experiments, we use several state-of-art anomaly detection algorithms in a comparison with our method using both simulated and real-world HSI datasets. The experimental results demonstrate that the proposed method outperforms others.

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A Hyperspectral Target Detection Framework With Subtraction Pixel Pair Features

Cited by 35 publications

References 25 publications

Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm

Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm

Continuous Particle Swarm Optimization-Based Deep Learning Architecture Search for Hyperspectral Image Classification

Hyperspectral Anomaly Detection via Graphical Connected Point Estimation and Multiple Support Vector Machines

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