Nonlinear Anomaly Detection Based on Spectral–Spatial Composite Kernel for Hyperspectral Images

Gao, Yenan; Cheng, Tongkai; Wang, Bin

doi:10.1109/lgrs.2020.2994629

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…Wu et al [18] proposed a novel kernel-based decomposition model with total variation and sparsity regularizations via union dictionary for nonlinear hyperspectral anomaly detection. Gao et al [19] proposed a nonlinear spectral-spatial composite kernel-based detector (SSCKD). It combines spectral and spatial features using a composite kernel function to enhance the anomaly detector's discriminatory capability.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral anomaly detection based on background purification and spectral feature extraction

Zhao,

Zheng,

2024

International Conference on Optical and Photonic Engineering (icOPEN 2023)

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Section: Introductionmentioning

confidence: 99%

Hyperspectral anomaly detection based on background purification and spectral feature extraction

Zhao,

Zheng,

2024

International Conference on Optical and Photonic Engineering (icOPEN 2023)

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“…In recent years, ML-based methods for hyperspectral anomaly detection have emerged continuously [44], [45]. Kernel methods [46], sparse representation models [47], discriminative subspace analysis, spectral data self-learning, and deep learning represent several major research directions [23]. The widespread application of ML-based method in hyperspectral anomaly detection exhibits strong analytical ability for complex models.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Based on Tree Topology for Hyperspectral Images

Sun

Zhang

Zhuang

et al. 2023

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

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As one of the most important research and application directions in hyperspectral remote sensing, anomaly detection (AD) aims to locate objects of interest within a specific scene by exploiting spectral feature differences between different types of land cover without any prior information. Most traditional AD algorithms are model-driven and describe hyperspectral data with specific assumptions, which cannot combat the distributional complexity of land covers in real scenes, resulting in a decrease in detection performance. To overcome the limitations of traditional algorithms, a novel tree topology-based anomaly detection (TTAD) method for hyperspectral images (HSIs) is proposed in this paper. TTAD departs from the single analytical mode based on specific assumptions but directly parses the HSI data itself. It makes full use of the "few and different" characteristics of anomalous data points that are sparsely distributed and far away from high-density populations. On this basis, topology, a powerful tool in mathematics that successfully handle multiple types of data mining tasks, is applied to anomaly detection to ensure sufficient feature extraction of land covers. First, the redistribution of HSI data is realized by constructing a tree-type topological space to improve the separability between anomalies and backgrounds. Then, topologically related subsets in this space are utilized to evaluate the abnormality degree of each sample in a data set, and detection results for the HSI are output accordingly. Abandoning traditional modeling but focusing on mining the data characteristics of HSI itself enables TTAD to better adapt to different complex scenes and locate anomalies with high precision. Experimental results on a large number of benchmark data sets demonstrate that TTAD could achieve excellent detection results with considerable computational efficiency. The proposed method exhibits superior comprehensive performance and is promising to be popularized in practical applications.

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“…H YPERSPECTRAL remote sensing images (HSIs) usually consist of hundreds of narrow and continuous bands, and thus can provide rich spectral and spatial information of ground objects. Currently, HSIs are applied in a wide range of applications such as environmental protection [1]- [3], anomaly detection [4]- [6], land cover analysis [7], [8], image segmentation [9], and hyperspectral classification [10]- [12]. For these applications, hyperspectral classification is a vital task used to identify different land covers that have distinct spectral differences.…”

Section: Introductionmentioning

confidence: 99%

Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

Ning,

Wang

2023

IEEE Access

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Hyperspectral band selection plays a key role for overcoming the curse of dimensionality in the classification of hyperspectral remote sensing images (HSIs). Recently, clustering-based band selection methods have demonstrated great potential to select informative and representative bands for hyperspectral classification tasks. However, most clustering-based methods perform clustering directly on the original high-dimensional data, which reduces their performance. To address this problem, a novel band selection method called global-local consistency constrained deep embedded clustering (GLC-DEC) is proposed in this paper. In GLC-DEC, to simultaneously learn the lowdimensional embedded representation and cluster assignments of all bands in an HSI, the stacked autoencoder is integrated with the K-means method. In addition, to reduce the adverse impact of a limited number of training samples available in HSIs, local and global consistency constraints are imposed on the embedded representation so that discriminatively consistent representation of all bands is learned. Specifically, local graph regularization and global graph regularization are introduced into the GLC-DEC model, by which the strong correlation between neighboring bands and the manifold structure of all bands are fully exploited. Based on the clustering results provided by GLC-DEC, a group of representative bands are selected by using the minimum noise method. Experimental results on two real datasets demonstrate that the proposed GLC-DEC outperformed several state-of-the-art methods.

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Nonlinear Anomaly Detection Based on Spectral–Spatial Composite Kernel for Hyperspectral Images

Cited by 13 publications

References 15 publications

Hyperspectral anomaly detection based on background purification and spectral feature extraction

Hyperspectral anomaly detection based on background purification and spectral feature extraction

Anomaly Detection Based on Tree Topology for Hyperspectral Images

Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

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