Ensemble and Random RX With Multiple Features Anomaly Detector for Hyperspectral Image

Yang, Xuemin; Huang, Xiaobei; Zhu, Mingjun; Xu, Sha; Liu, Yijun

doi:10.1109/lgrs.2022.3167758

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…Yang et al [ 25 ] proposed an Ensemble and Random RX with Multiple Features ( ERRX MFs ) anomaly detector. Three features, Gabor, Extended Morphological Profile (EMP), and Extended Multiattribute Profile (EMAP), were computed using heterogeneous base models mixed with the original hyperspectral image (passthrough).…”

Section: Related Workmentioning

confidence: 99%

“…They removed the noisy bands, normalized the remaining ones, randomly sub-sampled several subfeature sets, and used six different base models in the ensemble. Unlike the ERRX MFs algorithm proposed by Yang et al [ 25 ], SED evaluates the six base models to choose the best-performing methods and uses them to obtain an enhanced feature set. This enhanced feature set is combined with the original hyperspectral image (passthrough) to become the input for the PTA algorithm (meta-model).…”

Section: Related Workmentioning

confidence: 99%

“…Another strategy is to apply homogeneous outlier detectors to newly extracted features to create an ensemble. For example, Yang et al [ 25 ] proposed a voting average ensemble (ERRX MFs) where multiple feature extraction algorithms extracted the features from a hyperspectral image, and the RX detector used those features to create an ensemble. Alternatively, Wang et al [ 26 ] proposed an ensemble of sub-features (SED) by applying heterogeneous algorithms to the data to create new sub-features, and finally a prior-based tensor approximation algorithm (PTA [ 27 ]) used these sub-features for anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Greedy Ensemble Hyperspectral Anomaly Detection

Hossain,

Younis,

Robinson

et al. 2024

J. Imaging

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Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport–Beach–Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Greedy Ensemble Hyperspectral Anomaly Detection

Hossain,

Younis,

Robinson

et al. 2024

J. Imaging

View full text Add to dashboard Cite

show abstract

“…Early anomaly detection systems were mostly guided by statistical modeling techniques. An anomaly detector named Reed Xiaoli (RX) [ 19 , 20 , 21 , 22 ], in which the background is assumed to conform to the multivariable normal distribution, has been proposed. It uses the samples in the scene to estimate the model parameters and judges whether it is an abnormal pixel by using the Mahalanobis distance to measure the difference of the abnormal pixel.…”

Section: Introductionmentioning

confidence: 99%

Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery

Lian

Zhao

Zheng

et al. 2023

Sensors

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Anomaly detection of hyperspectral remote sensing data has recently become more attractive in hyperspectral image processing. The low-rank and sparse matrix decomposition-based anomaly detection algorithm (LRaSMD) exhibits poor detection performance in complex scenes with multiple background edges and noise. Therefore, this study proposes a weighted sparse hyperspectral anomaly detection method. First, using the idea of matrix decomposition in mathematics, the original hyperspectral data matrix is reconstructed into three sub-matrices with low rank, small sparsity and representing noise, respectively. Second, to suppress the noise interference in the complex background, we employed the low-rank, background image as a reference, built a local spectral and spatial dictionary through the sliding window strategy, reconstructed the HSI pixels of the original data, and extracted the sparse coefficient. We proposed the sparse coefficient divergence evaluation index (SCDI) as a weighting factor to weight the sparse anomaly map to obtain a significant anomaly map to suppress the background edge, noise, and other residues caused by decomposition, and enhance the abnormal target. Finally, abnormal pixels are segmented based on the adaptive threshold. The experimental results demonstrate that, on a real-scene hyperspectral dataset with a complicated background, the proposed method outperforms the existing representative algorithms in terms of detection performance.

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“…Inspired by the RX detector, many other methods have been proposed to make some improvements over the RX detector. Some works aim at formulating a more accurate distribution for the background, such as the local RX detector [19], the weighted RX detector [20], the subspace RX detector [21], kernel RX [22], and some other detectors [23], [24]. Meanwhile, some works mainly focus on enhancing the spatial discrimination ability of the input HSI.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Negative Sampling Autoencoder for Hyperspectral Anomaly Detection

Wang

Sun

Xia

et al. 2022

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

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Hyperspectral anomaly detection (HAD) aims at detecting the anomalies without any prerequisite information, which gains lots of attention in recent years. Most of existing detectors locate the anomalies by eliminating the background. The background is usually reconstructed by utilizing only the global and local homogenous attribute, no matter the matrix decompositionbased or the deep learning-based methods. In this article, a dynamic negative sampling autoencoder is proposed for the hyperspectral anomaly detection (DNA-HAD). Some pixels are randomly selected and altered as negative samples. Both the rest original pixels and the altered negative samples are sent into the network. An adaptive adjusted loss function is designed to suppress the reconstruction error for the original pixels, and to enlarge the error for the negative samples. Meanwhile, skip connection is designed to ensure features at both shallow levels and deep levels being utilized for the reconstructing process. In this way, by importing some negative samples in the reconstructing process, the proposed DNA-HAD is not only robust to reconstruct the background but also sensitive to detect the anomalies. Experiments on six hyperspectral imagery which are captured by different sensors have demonstrated the effectiveness of the proposed method.

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Ensemble and Random RX With Multiple Features Anomaly Detector for Hyperspectral Image

Cited by 11 publications

References 20 publications

Greedy Ensemble Hyperspectral Anomaly Detection

Greedy Ensemble Hyperspectral Anomaly Detection

Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery

Dynamic Negative Sampling Autoencoder for Hyperspectral Anomaly Detection

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