Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery

Guo, Qiaozhen; Zhang, Bing; Ran, Qiong; Gao, Lianru; Li, Jun; Plaza, Antonio

doi:10.1109/jstars.2014.2302446

Cited by 210 publications

(81 citation statements)

References 35 publications

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“…Since then many versions of RX-type anomaly detectors have been proposed including a correlation-matrix-based RX detector and some local detectors using a sliding window to make anomaly detection adaptive [6,8,[12][13][14].…”

Section: Commonly Used Global and Local Anomaly Detectorsmentioning

confidence: 99%

Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery

Zhao

Wang

et al. 2015

Remote Sensing

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Abstract:Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. A well-known algorithm for hyperspectral anomaly detection is the RX detector. A number of variations have been studied since then, including global and local versions for different type of anomalies. Aiming at a real-time requirement for practical applications, this paper extends the concept of global and local anomaly detectors to be real-time detectors. The algorithms exploit the fact that a true real-time detector must produce its output in a causal manner and at the same time as an input comes in. Taking advantage of the Woodbury matrix identity, the global and local real-time detectors can be implemented and processed pixel-by-pixel in real time. Both synthetic and real hyperspectral imagery are conducted to demonstrate their performance.

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Section: Commonly Used Global and Local Anomaly Detectorsmentioning

confidence: 99%

Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery

Zhao

Wang

et al. 2015

Remote Sensing

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“…Disadvantages of the method includes its requirement for high computational power, and the possible estimation errors that can occur based on the assumption that a suitable Gaussian model can be learned from a relatively smaller subset of the hyperspectral image. The former problem can be eased with parallel implementation of such a system [49,50,51,52,53,54].…”

Section: Anomaly Detectionmentioning

confidence: 99%

Anomaly detection with sparse unmixing and Gaussian mixture modeling of hyperspectral images

Erdinç

Aksoy

2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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One of the main applications of hyperspectral image analysis is anomaly detection where the problem of interest is the detection of small rare objects that stand out from their surroundings. A common approach to anomaly detection is to first model the background scene and then to use a detector that quantifies the difference of a particular pixel from this background. However, identifying the dominant background components and modeling them is a challenging task. We propose an anomaly detection framework that uses Gaussian mixture models for characterizing the scene background in hyperspectral images. First, the full spectrum is divided into several contiguous band groups for dimensionality reduction as well as for exploiting the peculiarities of different parts of the spectrum. Then, sparse spectral unmixing is performed for each band group for identifying significant endmembers in the scene. Three methods for identifying the dominant background groups such as thresholding, hierarchical clustering and biclustering are used in the endmember abundance space to retrieve the sets of pixel groups that represent dominant background components. Next, these pixel groups are used for initializing individual Gaussian mixture models that are estimated separately for each spectral band group. The proposed method enables automatic identification of the number of mixture components and effective initialization of the estimation procedure for the mixture model. Finally, the Gaussian mixture models for all groups are statistically fused for obtaining the final anomaly map for the scene. Comparative experiments showed that the proposed methods performed better than two other density-based anomaly detectors, especially for small false positive rates, on an airborne hyperspectral data set. Anomali tespiti, hiperspektral görüntü analizindeki ana uygulamalardan biridir. Anomali tespitindeki problem, görüntüde seyrek olarak bulunan,çevresine göre farklılık gösteren küçük nesnelerin tespitidir. Yaygın yaklaşımlardan biri görüntü arka planının modellenmesi ve sınanan pikselin bu modele olan farklılıgına göre sınıflandırılmasıdır. Ancak, karmaşık arka planların modellenmesi kolay degildir. Bu kapsamda, Gauss karışım modeli tabanlı bir anomali tespiti yöntemï onerilmektedir.Öncelikle görüntü belirli sayıda spektral gruplara ayrılmaktadır. Ardından seyrek spektral ayrıştırma bütün spektral grouplarda uygulanmaktadır. Sonrasında, elde edilen son eleman bolluk degerleri kullanılarak, eşikleme tabanlı, sıra düzenli tabanlı, veçift yönlüöbekleme tabanlı olmaküzereüç farklı yöntem ile baskın arka plan grupları tespit edilmektedir. Ardından, belirlenen arka plan gruplarını temsil eden pikseller başlangıç degerlerinin hesaplanmasında kullanılmaküzere, her bir spektral grup için bir Gauss karışım modelï ogrenilmektedir.Önerilen yöntem karışım modeli için bileşen sayısının otomatik belirlenmesine ve kestirim sürecinin etkili başlatılmasına olanak saglamaktadir. Son olarak,ögrenilen Gauss karışım modelleri istatistiksel olarak birleştirilerek sonu...

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“…The classic RX algorithm is based on the global sample covariance matrix K, and is referred to as K-RXD. Since then, many RX-like anomaly detectors have been proposed [7][8][9][10][11][12][13]. Of particular interest are RXD using global sample correlation matrix R (R-RXD) [7,8], and RXD based on local background covariance matrix (L-RXD) [9].…”

Section: Introductionmentioning

confidence: 99%

Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

et al. 2018

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Abstract:Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, the Woodbury identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors.

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Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery

Cited by 210 publications

References 35 publications

Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery

Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery

Anomaly detection with sparse unmixing and Gaussian mixture modeling of hyperspectral images

Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

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