Binary-Class Collaborative Representation for Target Detection in Hyperspectral Images

Zhu, Dehui; Du, Bo; Zhang, Liangpei

doi:10.1109/lgrs.2019.2893395

Cited by 32 publications

(16 citation statements)

References 19 publications

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“…Similarly, if the difference value is larger than certain threshold, the test pixel can be claimed as target, or background otherwise. Nevertheless, a key problem for many sparsity-based target detection methods is the constructions of target and background dictionaries [2,[26][27]. Generally, the target dictionary can be formed via the target training pixels that are selected from the global image scene, and the priori information is usually given by the target spectrum obtained from a target spectral library.…”

Section: A Srd and Srbbhmentioning

confidence: 99%

Learning Structurally Incoherent Background and Target Dictionaries for Hyperspectral Target Detection

Guo

Luo

Zhang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Existing sparsity-based hyperspectral image (HSI) target detection methods have two key problems, i.e., 1) The background dictionary is locally constructed by the pixels between the inner and outer windows, surrounding and enclosing the central test pixel. The dual-window strategy is intricate, and might result in impure background dictionary deteriorating the detection performance. 2) For an unbalanced binary classification problem, the target dictionary atoms are generally inadequate comparing with the background dictionary, which might yield unstable performance. For the issues, this paper proposes a novel Structurally Incoherent Background and Target Dictionaries (SIBTD) learning model for HSI target detection. Specifically, with the concept that the observed HSI data is composed of lowrank background, sparsely distributed targets and dense Gaussian noise, the background and target dictionaries can be jointly derived from the observed HSI data. Additionally, the introduction of structural incoherence can enhance the discrimination between the target and background dictionaries. Thus, the developed model can not only lead to a pure and unified background dictionary, but also augment the target dictionary for improved detection performance. Besides, an efficient optimization algorithm is devised to solve SIBTD model, and the performance of SIBTD is verified on three benchmark HSI datasets in comparison with several state-of-the-art detectors.

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Section: A Srd and Srbbhmentioning

confidence: 99%

Learning Structurally Incoherent Background and Target Dictionaries for Hyperspectral Target Detection

Guo

Luo

Zhang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…2. [12]. Taking into account low SNR, water absorption, and bad bands in raw data, the bands mentioned above are removed just like others for the sake of fairness.…”

Section: Experiments and Analysismentioning

confidence: 99%

Weighted Hierarchical Sparse Representation for Hyperspectral Target Detection

Wei

Jiang

Yuan

2020

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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Hyperspectral target detection has been widely studied in the field of remote sensing. However, background dictionary building issue and the correlation analysis of target and background dictionary issue have not been well studied. To tackle these issues, a Weighted Hierarchical Sparse Representation for hyperspectral target detection is proposed. The main contributions of this work are listed as follows. 1) Considering the insufficient representation of the traditional background dictionary building by dual concentric window structure, a hierarchical background dictionary is built considering the local and global spectral information simultaneously. 2) To reduce the impureness impact of background dictionary, target scores from target dictionary and background dictionary are weighted considered according to the dictionary quality. Three hyperspectral target detection data sets are utilized to verify the effectiveness of the proposed method. And the experimental results show a better performance when compared with the state-of-the-arts.

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“…Among these applications, target detection is considered as a fundamental task and has received a surge of interest [10], [11]. Essentially, target detection can be regarded as a problem of classification and localization [12], [13], which has been widely used spanning from civilian to military.…”

Section: Introductionmentioning

confidence: 99%

Background Learning Based on Target Suppression Constraint for Hyperspectral Target Detection

Xie

Zhang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral target detection is critical in both military and civilian applications. However, it is a challenging task due to the complexity of background and the limited samples of target in hyperspectral images (HSIs). In this paper, we propose a novel background learning model, called background learning based on target suppression constraint (BLTSC) to characterize high-dimensional spectral vectors. Considering insufficient target samples, the model is trained only on the background spectral samples to accurately learn the background distribution. Then the discrepancy between the reconstructed and original HSIs are examined to spot the targets. To obtain a background training dataset, coarse detection is carried out. However, it is quite difficult to retrieve pure background data. Thus a target suppression constraint is imposed to reduce the impact of suspected target samples on background reconstruction. Experiments on six real HSIs demonstrate that the proposed framework significantly outperforms the current state-of-the-art detection methods and yields higher detection accuracy and lower false alarm rate. Index Terms-Hyperspectral image (HSI), target detection, background learning, target suppression constraint.

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Binary-Class Collaborative Representation for Target Detection in Hyperspectral Images

Cited by 32 publications

References 19 publications

Learning Structurally Incoherent Background and Target Dictionaries for Hyperspectral Target Detection

Learning Structurally Incoherent Background and Target Dictionaries for Hyperspectral Target Detection

Weighted Hierarchical Sparse Representation for Hyperspectral Target Detection

Background Learning Based on Target Suppression Constraint for Hyperspectral Target Detection

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