scholarly journals Adaptive multi-layer structure with spatial-spectrum combination for hyperspectral image anomaly detection

2021 ◽  
Vol 39 (3) ◽  
pp. 484-491
Author(s):  
Hongmei Yan ◽  
Mingyi He ◽  
Hanxue Mei
Keyword(s):  
Spatial Structure ◽  
Anomaly Detection ◽  
Hyperspectral Image ◽  
Layer Structure ◽  
Spatial Spectrum ◽  
Spectral Dimension ◽  
Background Information ◽  
Background Suppression ◽  
Spectral Difference ◽  
Present Algorithm

A new algorithm for hyperspectral image anomaly detection is proposed by designing an adaptive multi-layer structure with spatial-spectral combination information, which is different from the traditional anomaly detection algorithms only considering the spectral difference between the anomaly point and the background pixels, and ignoring the difference between the local spatial structure and spectrum. Firstly, the present algorithm not only calculates the spectral dimension difference between the pixels to be measured and the pixels in the background window, but also measures the spatial structure difference between the internal window and the background window. Mostly, an adaptive multi-layer structure for anomaly detection framework is carried out based on the idea of background suppression, and a multi-layered anomaly detector is constructed. The anomaly detection results of each layer of the detector are taken as the constraints, and the background information of the image input in the detector of the next layer is suppressed, adaptively suppressing the background noises. The experimental results show that the present algorithm makes better use of both the local spatial structure and the spectral dimension information than the traditional two-window models (global RX, local RX and KRX), adaptively suppresses background, reduces the false alarm rate, and improves the detection effect of the abnormal targets with fewer pixels.

scholarly journals Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

2019 ◽  
Vol 11 (21) ◽  
pp. 2537 ◽  
Author(s):  
Dandan Ma ◽  
Yuan Yuan ◽  
Qi Wang
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Hyperspectral Image ◽  
Superior Performance ◽  
Detection Methods ◽  
Background Information ◽  
Detection Accuracy ◽  
Layer By Layer ◽  
Edge Preserving ◽  
Image Pixels

A hyperspectral image usually covers a large scale of ground scene, which contains various materials with different spectral properties. When directly exploring the background information using all the image pixels, complex spectral interactions and inter-/intra-difference of different samples will significantly reduce the accuracy of background evaluation and further affect the detection performance. To address this problem, this paper proposes a novel hyperspectral anomaly detection method based on separability-aware sample cascade model. Through identifying separability of hyperspectral pixels, background samples are sifted out layer-by-layer according to their separable degrees from anomalies, which can ensure the accuracy and distinctiveness of background representation. First, as spatial structure is beneficial for recognizing target, a new spectral–spatial feature extraction technique is used in this work based on the PCA technique and edge-preserving filtering. Second, depending on different separability computed by sparse representation, samples are separated into different sets which can effectively and completely reflect various characteristics of background across all the cascade layers. Meanwhile, some potential abnormal targets are removed at each selection step to avoid their effects on subsequent layers. Finally, comprehensively taking different good properties of all the separability-aware layers into consideration, a simple multilayer anomaly detection strategy is adopted to obtain the final detection map. Extensive experimental results on five real-world hyperspectral images demonstrate our method’s superior performance. Compared with seven representative anomaly detection methods, our method improves the average detection accuracy with great advantages.

scholarly journals Anomaly Detection in Airborne Fourier Transform Thermal Infrared Spectrometer Images Based on Emissivity and a Segmented Low-Rank Prior

2021 ◽  
Vol 13 (4) ◽  
pp. 754
Author(s):  
Xuhe Zhu ◽  
Liqin Cao ◽  
Shaoyu Wang ◽  
Lyuzhou Gao ◽  
Yanfei Zhong
Keyword(s):  
Fourier Transform ◽  
Anomaly Detection ◽  
Mahalanobis Distance ◽  
Hyperspectral Imagery ◽  
Matrix Decomposition ◽  
Thermal Infrared ◽  
Low Rank ◽  
Background Information ◽  
Spectral Difference ◽  
Infrared Spectrometer

Although hyperspectral anomaly detection is commonly conducted in the visible, near-infrared, and shortwave infrared spectral regions, there has been less research on hyperspectral anomaly detection in the longwave infrared (LWIR) hyperspectral region. The radiance of thermal infrared hyperspectral imagery is determined by the temperature and emissivity. To avoid the detection uncertainty caused by the single factor of temperature, emissivity can be introduced to detect anomalies. However, in the emissivity domain, the spectral contrast and signal-to-noise ratio (SNR) are low, which makes it difficult to separate the anomalies from the background. In this paper, an anomaly detection method combining emissivity and a segmented low-rank prior (EaSLRP) is proposed for use with thermal infrared hyperspectral imagery. The EaSLRP method is divided into three parts—1) temperature/emissivity retrieval, 2) extraction of the thermal infrared hyperspectral background information, and 3) Mahalanobis distance detection. A homogeneous region generation method is also proposed to solve the problem of the complex global background leading to inaccurate background estimation. The GoDec method is used for matrix decomposition and background information extraction and to remove some of the noise. The proposed Mahalanobis distance detector then uses the background component and original image for anomaly detection, while highlighting the spectral difference between the anomalies and background. This method can also suppress the influence of noise, to some extent. The experimental results obtained with airborne Fourier transform thermal infrared spectrometer hyperspectral images demonstrate that the EaSLRP method is effective when compared with the Reed–Xiaoli detector (RXD), the segmented RX detector (SegRX), the low-rank and sparse representation-based detector (LRASR), the low-rank and sparse matrix decomposition (LRaSMD)-based Mahalanobis distance method (LSMAD), and the locally enhanced low-rank prior method (LELRP-AD).

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

2021 ◽  
Vol 13 (4) ◽  
pp. 721
Author(s):  
Zhongheng Li ◽  
Fang He ◽  
Haojie Hu ◽  
Fei Wang ◽  
Weizhong Yu
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Collaborative Representation ◽  
Detection Accuracy ◽  
Spatial Features ◽  
Collective Representation ◽  
Detection Approach ◽  
Adaptive Weight ◽  
Gabor Feature

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.

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

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3627 ◽  
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.

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

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.

Anomaly detection based on PCA and local RXOSP in hyperspectral image

2016 ◽  
Author(s):  
Juan Lin ◽  
Kun Gao ◽  
Lijing Wang ◽  
Xuemei Gong
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Image
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