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

Erdinç, Acar; Aksoy, Selim

doi:10.1109/igarss.2015.7326964

Cited by 9 publications

(6 citation statements)

References 55 publications

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“…In fact, anomalies usually refer to pixels that are less distributed and not similar to the neighbor pixels [42], which is the main analysis and basic understanding of the pixel level view. Moreover, when it comes to the subpixel level, the dissimilarities between the anomaly and background are mainly reflected in different endmembers.…”

Section: Dual-view Anomaly Hyperspectral Detection Via Spatial Consis...mentioning

confidence: 99%

“…Acar et al believed that anomalies are small, rare objects or materials with different spectral characteristics compared to their surroundings. They introduced an anomaly detection method using sparse unmixing and a Gaussian mixture model for the HSI [42]. These methods mentioned above provide a novel research direction for detecting anomalies, with many of them utilizing unmixing technology to extract features for further detection.…”

Section: Introductionmentioning

confidence: 99%

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Dual-View Hyperspectral Anomaly Detection via Spatial Consistency and Spectral Unmixing

Zhang

Jiao

2023

Remote Sensing

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Anomaly detection is a crucial task for hyperspectral image processing. Most popular methods detect anomalies at the pixel level, while a few algorithms for anomaly detection only utilize subpixel level unmixing technology to extract features without fundamentally analyzing the anomalies. To better detect and separate the anomalies from the background, this paper proposes a dual-view hyperspectral anomaly detection method by taking account of the anomaly analysis at both levels mentioned. At the pixel level, the spectral angular distance is adopted to calculate the similarities between the central pixel and its neighbors in order to further mine the spatial consistency for anomaly detection. On the other hand, from the aspect of the subpixel level analysis, it is considered that the difference between the anomaly and the background usually arises from dissimilar endmembers, where the unmixing will be fully implemented. Finally, the detection results of both views are fused to obtain the anomalies. Overall, the proposed algorithm not only interprets and analyzes the anomalies from dual levels, but also fully employs the unmixing for anomaly detection. Additionally, the performance of multiple data sets also confirmed the effectiveness of the proposed algorithm.

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Section: Dual-view Anomaly Hyperspectral Detection Via Spatial Consis...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dual-View Hyperspectral Anomaly Detection via Spatial Consistency and Spectral Unmixing

Zhang

Jiao

2023

Remote Sensing

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“…Sonuçların birleştirilmesi için ise pikseller arası enbüyükleme yapılarak herhangi bir bant grubunda anomali olarak karşımıza çıkan pikseller belirlenir. Herbir piksel için buşekilde hesaplanan olasılık degerleri ile ters orantılı olarak anomali olasılıgı atanır [11]. Önerilen çizge kesit yönteminde geliştirilen anomali tespit yöntemi arkaplan modellemesi için kullanılmıştır.…”

Section: B Arkaplan Modeliunclassified

Anomaly based target detection in hyperspectral images via graph cuts

Bati¹,

Erdinç

Çeşmeci

et al. 2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe -Hiperspektral hedef tespiti için yürütülen çalışmalar genel olarak iki sınıfta degerlendirilebilir.İlk sınıf olan anomali tespit yöntemlerinde, hedefin görüntünün geri kalanından farklı oldugu bilgisi kullanılarak görüntü analiz edilmektedir. Diger sınıfta ise daha önceden bilgisi edinilmiş hedefe ait spektral imza ile görüntüdeki herbir piksel arasındaki benzerlik bulunarak hedefin konumu tespit edimektedir. Her iki sınıf yöntemin de önemli bir dezavantajı hiperspektral görüntü piksellerini bagımsız olarak degerlendirip, aralarındaki komşuluk ilişkilerini göz ardı etmesidir. Bu makalede anomali tespit ve imza tabanlı tespit yaklaşımlarını, pikseller arası komşuluk ilişkilerini de göz önünde bulundurarak birleştiren çizge yaklaşımına dayalı yeni bir yöntem önerilmiştir. Hedeflerin hem imza bilgisine sahip olundugu hem de anomali sayılabilecek ölçülerde oldugu varsayılarak önerilen çizge yaklaşımında önplan için imza bilgisi kullanan özgün bir türev tabanlı uyumlu filtre önerilmiştir. Arkaplan için ise seyreklik bilgisi kullanarak Gauss karışım bileşeni kestirimi yapan yeni bir anomali tespit yöntemi geliştirilmiştir. Son olarak komşular arası benzerligi tanımlamak için ise spektral bir benzerlik ölçütü olan spektral açı eşleştiricisi kullanılmıştır. Önerilen çizge tabanlı yöntemin önplan, arkaplan ve komşuluk ilişkilerini uygunşekilde birleştirdigi ve önceki yöntemlere göre hedefi gürültüden arınmış bir bütünşeklinde başarıyla tespit edebildigi gözlemlenmiştir.Anahtar Kelimeler-çizge kesit, anomali, spektral imza, hiperspektral imge Abstract-The studies on hyperspectral target detection until now, has been treated in two approaches. Anomaly detection can be considered as the first approach, which analyses the hyperspectral image with respect to the difference between target and the rest of the hyperspectral image. The second approach compares the previously obtained spectral signature of the target with the pixels of the hyperspectral image in order to localize the target. A distinctive disadvantage of the aforementioned approaches is to treat each pixel of the hyperspectral image individually, without considering the neighbourhood relations between the pixels. In this paper, we propose a target detection algorithm which combines the anomaly detection and signature based hyperspectral target detection approaches in a graph based framework by utilizing the neighbourhood relations between the pixels. Assuming that the target signature is available and the target sizes are in the range of anomaly sizes, a novel derivative based matched filter is first proposed to model the foreground. Second, a new anomaly detection method which models the background as a Gaussian mixture is developed. The developed model estimates the optimal number of components forming the Gaussian mixture by means of utilizing sparsity information. Finally, the similarity of the neighbouring hyperspectral pixels is measured with the spectral angle mapper. The overall proposed graph based method has successfully combined the foreground, b...

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“…Although the above-mentioned sparse representation-based methods have achieved good performance, an important physical phenomenon has been neglected: mixed pixels are very common in HSIs. Hyperspectral unmixing technology enables an accurate characterization of the background features in AD [26,27]. The method proposed by Qu et al [27] realized the hyperspectral AD through spectral unmixing and dictionary-based low-rank decomposition.…”

Section: Introductionmentioning

confidence: 99%

Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

Zhao

Li²,

Zhang³

et al. 2021

Remote Sensing

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Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract the pure background features and to establish a structured sparse representation model at a sub-pixel level by using the Archetypal Analysis (AA) scheme. Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures. Moreover the unmixing reconstruction error is utilized for the identification of the target. Structured sparse representation is also adopted for anomaly target detection by using the background endmember features from AA unmixing. Moreover, both the AA unmixing reconstruction error and the structured sparse representation reconstruction error are integrated together to enhance the anomaly target detection performance. The proposed method exploits background features at a sub-pixel level to improve the accuracy of anomaly target detection. Comparative experiments and analysis on public hyperspectral datasets show that the proposed algorithm potentially surpasses all the counterpart methods in anomaly target detection.

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

Cited by 9 publications

References 55 publications

Dual-View Hyperspectral Anomaly Detection via Spatial Consistency and Spectral Unmixing

Dual-View Hyperspectral Anomaly Detection via Spatial Consistency and Spectral Unmixing

Anomaly based target detection in hyperspectral images via graph cuts

Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

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