Hyperspectral image betweenness centrality clustering based adaptive spatial and spectral neighborhood approach for anomaly detection

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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Anomaly Detection in Hyperspectral Image Based on SVDD Combined with Features Compression

10.1145/3461353.3461387 ◽

2021 ◽

Author(s):

Ping Ma ◽

Chengfei Yao ◽

Yang Li ◽

Jingang Ma

Keyword(s):

Anomaly Detection ◽

Hyperspectral Image

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A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images

Sensors ◽

10.3390/s18113627 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3627 ◽

Cited By ~ 1

Author(s):

Yi Zhang ◽

Zebin Wu ◽

Jin Sun ◽

Yan Zhang ◽

Yaoqin Zhu ◽

...

Keyword(s):

Anomaly Detection ◽

Sparse Representation ◽

Parallel Algorithm ◽

Hyperspectral Image ◽

Hyperspectral Images ◽

Low Rank ◽

Detection Methods ◽

Matrix Computations ◽

Hyperspectral Image Processing ◽

Mapreduce Model

Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA’s efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.

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Local density-based anomaly detection in hyperspectral image

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.9.095070 ◽

2015 ◽

Vol 9 (1) ◽

pp. 095070 ◽

Cited By ~ 7

Author(s):

Chen Lou ◽

Huijie Zhao

Keyword(s):

Anomaly Detection ◽

Hyperspectral Image ◽

Local Density

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

10.36227/techrxiv.14428880.v3 ◽

2021 ◽

Author(s):

Xiangyu Song ◽

Sunil Aryal ◽

Kai Ming Ting ◽

zhen Liu ◽

Bin He

Keyword(s):

Anomaly Detection ◽

Spatial Information ◽

Hyperspectral Image ◽

Gabor Filter ◽

Poor Performance ◽

Hyperspectral Data ◽

Relative Mass ◽

Anomaly Score ◽

Spectral Anomaly ◽

Isolation Forest

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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Anomaly detection based on PCA and local RXOSP in hyperspectral image

10.1117/12.2243816 ◽

2016 ◽

Author(s):

Juan Lin ◽

Kun Gao ◽

Lijing Wang ◽

Xuemei Gong

Keyword(s):

Anomaly Detection ◽

Hyperspectral Image

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An Improved Hyperspectral Image Anomaly Detection Algorithm using Low-Rank Representation

10.1145/3487075.3487170 ◽

2021 ◽

Author(s):

Dongying Bai ◽

Dongli Tang ◽

Hailin Tian ◽

Zhaozhan Li

Keyword(s):

Anomaly Detection ◽

Hyperspectral Image ◽

Detection Algorithm ◽

Low Rank ◽

Low Rank Representation

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Hyperspectral Anomaly Detection with Harmonic Analysis and Low-Rank Decomposition

Remote Sensing ◽

10.3390/rs11243028 ◽

2019 ◽

Vol 11 (24) ◽

pp. 3028 ◽

Cited By ~ 1

Author(s):

Pei Xiang ◽

Jiangluqi Song ◽

Huan Li ◽

Lin Gu ◽

Huixin Zhou

Keyword(s):

Anomaly Detection ◽

Harmonic Analysis ◽

Hyperspectral Image ◽

Low Rank ◽

Superior Performance ◽

Alternative Methods ◽

Qualitative Assessment ◽

Redundant Information ◽

Rank Decomposition ◽

Low Rank Decomposition

Hyperspectral anomaly detection methods are often limited by the effects of redundant information and isolated noise. Here, a novel hyperspectral anomaly detection method based on harmonic analysis (HA) and low rank decomposition is proposed. This paper introduces three main innovations: first and foremost, in order to extract low-order harmonic images, a single-pixel-related HA was introduced to reduce dimension and remove redundant information in the original hyperspectral image (HSI). Additionally, adopting the guided filtering (GF) and differential operation, a novel background dictionary construction method was proposed to acquire the initial smoothed images suppressing some isolated noise, while simultaneously constructing a discriminative background dictionary. Last but not least, the original HSI was replaced by the initial smoothed images for a low-rank decomposition via the background dictionary. This operation took advantage of the low-rank attribute of background and the sparse attribute of anomaly. We could finally get the anomaly objectives through the sparse matrix calculated from the low-rank decomposition. The experiments compared the detection performance of the proposed method and seven state-of-the-art methods in a synthetic HSI and two real-world HSIs. Besides qualitative assessment, we also plotted the receiver operating characteristic (ROC) curve of each method and report the respective area under the curve (AUC) for quantitative comparison. Compared with the alternative methods, the experimental results illustrated the superior performance and satisfactory results of the proposed method in terms of visual characteristics, ROC curves and AUC values.

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