Shearlet transform based anomaly detection for hyperspectral image

Zhou, Huixin; Niu, Xiaoxue; Qin, Hanlin; Zhou, Jun; Lai, Rui; Wang, Bingjian

doi:10.1117/12.978636

Cited by 2 publications

(2 citation statements)

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“…The shearlet can fix both the locations of singularities and the singularities' curve tracking automatically. For a > 0, s ∈ R, t ∈ R 2 , the ST can be defined using the following expression [23]:…”

Section: Shearlet Transformmentioning

confidence: 99%

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

2018

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This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images. The images are described via three memberships: true, indeterminate, and false memberships. An indeterminate filter is then defined in the neutrosophic set for reducing the indeterminacy of the images. A neutrosophic c-means clustering algorithm is applied to segment the dermoscopic images. With the clustering results, skin lesions are identified precisely using an adaptive region growing method. To evaluate the performance of this algorithm, a public data set (ISIC 2017) is employed to train and test the proposed method. Fifty images are randomly selected for training and 500 images for testing. Several metrics are measured for quantitatively evaluating the performance of NCARG. The results establish that the proposed approach has the ability to detect a lesion with high accuracy, 95.3% average value, compared to the obtained average accuracy, 80.6%, found when employing the neutrosophic similarity score and level set (NSSLS) segmentation approach.

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Section: Shearlet Transformmentioning

confidence: 99%

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

2018

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“…In Ref. 58, the authors detected anomaly in hyperspectral images by, first, using the shearlet to decompose the images into several directional sub-bands at multiple scales. Then, in each sub-band, the background signal is reduced and the fourth-order partial differential equation is applied to brighten up the anomaly.…”

Section: Then They Calculated the Change Map As Illustrated By Eq (mentioning

confidence: 99%

Review of multiscale geometric decompositions in a remote sensing context

Zaouali¹,

Bouzidi²,

Zagrouba³

2016

J. Electron. Imaging

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Abstract. The quest for optimal representations is considered a challenging goal in the field of image processing. This consists of reducing the processing's complexity while ensuring an efficient reconstruction. An optimal representation should conserve the properties of the image pertaining to smooth content and contours. The multiscale geometric decompositions (MGD) were designed to reach this finality. They were used in many fields and for different purposes, such as feature extraction, detail enhancing, and change detection. A state-of-art of these decompositions is proposed in this paper. We present their theoretical definitions and how they capture the feature of the objects within an image. An overview table is elaborated where we summarize the methods, the data and the different criteria of assessment used in the studied cases. We are interested, particularly, in the use of MGD in a remote sensing (RS) context. Thus, some examples of their applications on RS images are studied. A discussion is presented based on the analyzed cases.

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Shearlet transform based anomaly detection for hyperspectral image

Cited by 2 publications

References 10 publications

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

Review of multiscale geometric decompositions in a remote sensing context

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