Multiple Features and Isolation Forest-Based Fast Anomaly Detector for Hyperspectral Imagery

Wang, Rong; Nie, Feiping; Wang, Zhen; He, Fang; Li, Xuelong

doi:10.1109/tgrs.2020.2978491

Cited by 42 publications

(21 citation statements)

References 59 publications

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“…In our experiments, the anomaly detection performance of the proposed SSIIFD is evaluated and compared to that of five state-of-the-art detectors: RX [6], LRASR [13], PTA [15], KIFD [24], and MFIFD [28]. Specifically, RX is a representative statistical modeling-based technique.…”

Section: Comparison Methods and Evaluation Indexesmentioning

confidence: 99%

Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

Song¹,

Aryal²,

Ting³

et al. 2021

Preprint

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Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods.

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Section: Comparison Methods and Evaluation Indexesmentioning

confidence: 99%

Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

Song¹,

Aryal²,

Ting³

et al. 2021

Preprint

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“…Indeed, they offer the flexibility to learn a model from an initial (regular) data distribution and are able to flag data that significantly differ from the learned distribution. For this reason, in order to evaluate the performance obtained by ECHAD, in our experiments we considered three state-of-the-art competitor methods falling in this class, namely One-Class SVM [2], [33]- [35], Isolation Forest [3], [36], [37], and LOF [4], [38], [39], which are widely adopted in the recent literature, and are shown to provide highly accurate predictions.…”

Section: B Experimental Setupmentioning

confidence: 99%

“…In the literature, several one-class classification methods have been proposed. Among them, it is worth mentioning One-Class SVM [2], [32]- [35], Isolation Forest [3], [36], [37], One-Class Local Outlier Factor (LOF) [4], [38], [39] and approaches based on autoencoders [5], [6].…”

Section: Introductionmentioning

confidence: 99%

ECHAD: Embedding-Based Change Detection From Multivariate Time Series in Smart Grids

et al. 2020

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Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this paper, we propose a novel change detection method, called ECHAD (Embedding-based CHAnge Detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with stateof-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections.

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“…The global RX (GRX) and local RX (LRX) are two versions of the RX algorithm. The GRX method uses the whole image to model the background, while LRX utilizes the local dual-window to model the background [27]. The kernel RX (KRX) [28] algorithm is a nonlinear version of the RX algorithm that uses kernel theory to transform every pixel to a high-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

“…These methods do not require any statistical assumptions and have, thus, attracted significant attention [20]. These techniques make use of the conspicuous characteristics of anomalies: the low probability of occurrence and the different spectral signature from the background pixels [27]. There are several forms of representation-based methods, including the sparse representation-based methods [1,[31][32][33][34][35][36][37], the low-rank methods [38][39][40][41][42][43], and the collaborative methods [44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Random Collective Representation-Based Detector with Multiple Features for Hyperspectral Images

et al. 2021

Remote Sensing

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Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD). However, the sliding dual window of the original CRD introduces high computational complexity. Moreover, most HAD models only consider a single spectral or spatial feature of the hyperspectral image (HSI), which is unhelpful for improving detection accuracy. To solve these problems, in terms of speed and accuracy, we propose a novel anomaly detection approach, named Random Collective Representation-based Detector with Multiple Feature (RCRDMF). This method includes the following steps. This method first extract the different features include spectral feature, Gabor feature, extended multiattribute profile (EMAP) feature, and extended morphological profile (EMP) feature matrix from the HSI image, which enables us to improve the accuracy of HAD by combining the multiple spectral and spatial features. The ensemble and random collaborative representation detector (ERCRD) method is then applied, which can improve the anomaly detection speed. Finally, an adaptive weight approach is proposed to calculate the weight for each feature. Experimental results on six hyperspectral datasets demonstrate that the proposed approach has the superiority over accuracy and speed.

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Multiple Features and Isolation Forest-Based Fast Anomaly Detector for Hyperspectral Imagery

Cited by 42 publications

References 59 publications

Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

ECHAD: Embedding-Based Change Detection From Multivariate Time Series in Smart Grids

Random Collective Representation-Based Detector with Multiple Features for Hyperspectral Images

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