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

Ma, Dandan; Yuan, Yuan; Wang, Qi

doi:10.3390/rs10050745

Cited by 57 publications

(24 citation statements)

References 55 publications

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“…To solve the problem, this study proposes SALP algorithm, and its core idea can be described as follows: First, the sparse graph that explicitly considers the influence of data noise is insensitive to the gathered label noise, and its sparsity is datum-adaptive instead of manually determining the size of neighborhood [46][47][48]. Second, spatial information is important for measuring the similarity of different pixels.…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification

2019

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In a real hyperspectral image classification task, label noise inevitably exists in training samples. To deal with label noise, current methods assume that noise obeys the Gaussian distribution, which is not the real case in practice, because in most cases, we are more likely to misclassify training samples at the boundaries between different classes. In this paper, we propose a spectral–spatial sparse graph-based adaptive label propagation (SALP) algorithm to address a more practical case, where the label information is contaminated by random noise and boundary noise. Specifically, the SALP mainly includes two steps: First, a spectral–spatial sparse graph is constructed to depict the contextual correlations between pixels within the same superpixel homogeneous region, which are generated by superpixel image segmentation, and then a transfer matrix is produced to describe the transition probability between pixels. Second, after randomly splitting training pixels into “clean” and “polluted,” we iteratively propagate the label information from “clean” to “polluted” based on the transfer matrix, and the relabeling strategy for each pixel is adaptively adjusted along with its spatial position in the corresponding homogeneous region. Experimental results on two standard hyperspectral image datasets show that the proposed SALP over four major classifiers can significantly decrease the influence of noisy labels, and our method achieves better performance compared with the baselines.

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Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification

2019

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“…Since HSIs reflect rich spectral and spatial resolution, they offer the potential to discriminate more detailed classes and provide even broader applications for land-over classification and clustering [5][6][7][8]. To a certain extent, dealing with HSIs is difficult because the numerous spectral bands significantly increase the computational complexity and the noise in HSIs can badly influence the classification accuracy [9,10]. The existing work reported by most scholars can be roughly divided into two categories according to whether a certain number of training samples are required, as demonstrated in [11,12]: (1) supervised learning named HSI classification; and (2) unsupervised learning named HSI clustering.…”

Section: Introductionmentioning

confidence: 99%

Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification

2019

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Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance. In the past years, a growing number of advanced hyperspectral remote sensing image classification techniques based on manifold learning, sparse representation and deep learning have been proposed and reported a good performance in accuracy and efficiency on state-of-the-art public datasets. However, most existing methods still face challenges in dealing with large-scale hyperspectral image datasets due to their high computational complexity. In this work, we propose an improved spectral clustering method for large-scale hyperspectral image classification without any prior information. The proposed algorithm introduces two efficient approximation techniques based on Nyström extension and anchor-based graph to construct the affinity matrix. We also propose an effective solution to solve the eigenvalue decomposition problem by multiplicative update optimization. Experiments on both the synthetic datasets and the hyperspectral image datasets were conducted to demonstrate the efficiency and effectiveness of the proposed algorithm.

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“…Dictionary construction is an important process in many HSI problems and there are many ways to implement it. [30] proposes an AD method based on sparse presentation through constructing multiple dictionaries to learn discriminative features. In each category, the representative spectra that can significantly enhance the difference between background and anomalies are selected.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Dictionary Construction-Based Low-Rank Representation and Adaptive Weighting

et al. 2019

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Anomaly detection (AD), which aims to distinguish targets with significant spectral differences from the background, has become an important topic in hyperspectral imagery (HSI) processing. In this paper, a novel anomaly detection algorithm via dictionary construction-based low-rank representation (LRR) and adaptive weighting is proposed. This algorithm has three main advantages. First, based on the consistency with AD problem, the LRR is employed to mine the lowest-rank representation of hyperspectral data by imposing a low-rank constraint on the representation coefficients. Sparse component contains most of the anomaly information and can be used for anomaly detection. Second, to better separate the sparse anomalies from the background component, a background dictionary construction strategy based on the usage frequency of the dictionary atoms for HSI reconstruction is proposed. The constructed dictionary excludes possible anomalies and contains all background categories, thus spanning a more reasonable background space. Finally, to further enhance the response difference between the background pixels and anomalies, the response output obtained by LRR is multiplied by an adaptive weighting matrix. Therefore, the anomaly pixels are more easily distinguished from the background. Experiments on synthetic and real-world hyperspectral datasets demonstrate the superiority of our proposed method over other AD detectors.

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

Cited by 57 publications

References 55 publications

Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification

Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification

Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification

Hyperspectral Anomaly Detection via Dictionary Construction-Based Low-Rank Representation and Adaptive Weighting

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