Fractional Fourier Transform-Based Tensor RX for Hyperspectral Anomaly Detection

Zhang, Lili; Ma, Jiachen; Cheng, Baozhi; Lin, Fen

doi:10.3390/rs14030797

Cited by 13 publications

(2 citation statements)

References 33 publications

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“…For another thing, the background modeling (i.e., calculating the statistical characteristics) is vulnerable to being contaminated by the anomalies, thus resulting in suboptimal modeling effect. To tackle above issues, some variants based on RX detector, such as local RX (LRX) [21], kernel RX (KRX) [22], fractional Fourier entropy RX (FrFE-RX) [23] and fractional Fourier transform-based tensor RX (FrFT-TRX) [24], etc., are proposed. However, these methods fail to fully characterize the background.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation

Lin

Zhang

Cheng

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Sparse representation (SR)-based approaches and collaborative representation (CR)-based methods are proved to be effective to detect the anomalies in a hyperspectral image (HSI). Nevertheless, the existing methods for achieving hyperspectral anomaly detection (HAD) generally only consider one of them, failing to comprehensively exploit them to further promote the detection performance. To address the issue, a novel HAD method, which integrates both sparse representation and collaborative representation (SRCR), is proposed in this paper. To be specific, a SR model, whose overcomplete dictionary is generated by means of the density-based clustering algorithm and superpixel segmentation method, is firstly constructed for each pixel in an HSI. Then, for each pixel in an HSI, the used atoms in SR model are sifted to form the background dictionary corresponding to the CR model. To fully exploit both SR and CR information, we further combine the residual features obtained from both SR and CR model by the nonlinear transformation function to generate the response map. Finally, to preserve contour information of the objects, a postprocessing operation with guided filter is imposed into the response map to acquire the detection result. Experiments conducted on simulated and real data sets demonstrate that the proposed SRCR outperforms the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation

Lin

Zhang

Cheng

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…Hyperspectral images (HSIs), which have the characteristics of a wide spectral range and high spectral resolution, are widely utilized to discriminate physical properties of different materials [1]. Benefitting from the rich spectral information, HSIs are active in the field of image classification [2,3], hyperspectral unmixing [4,5], band selection [6,7], anomaly detection [8,9] and target detection [10,11]. Among these applications, hyperspectral anomaly detection (HAD), aiming to excavate the pixels with significant spectral difference relative to surrounding pixels [12], attracts particular interest.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Dual Dictionaries Construction Guided by Two-Stage Complementary Decision

et al. 2022

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Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the background pixels in the above methods, and this limits the effect of hyperspectral anomaly detection (HAD). In this paper, a dual dictionaries construction method via two-stage complementary decision (DDC–TSCD) for HAD is proposed. In the first stage, an adaptive inner window–based saliency detection was proposed to yield a coarse binary map, acting as the indicator to select pure background pixels. For the second stage, a background estimation network was designed to generate a fine binary map. Finally, the coarse binary map and fine binary map worked together to construct a pure background dictionary and potential anomaly dictionary in the guidance of the superpixels derived from the first stage. The experiments conducted on public datasets (i.e., HYDICE, Pavia, Los Angeles, San Diego-I, San Diego-II and Texas Coast) demonstrate that DDC–TSCD achieves satisfactory AUC values, which are separately 0.9991, 0.9951, 0.9968, 0.9923, 0.9986 and 0.9969, as compared to four typical methods and three state-of-the-art methods.

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A joint model based on graph and deep learning for hyperspectral anomaly detection

Zhang,

Lin,

2024

Infrared Physics & Technology

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Fractional Fourier Transform-Based Tensor RX for Hyperspectral Anomaly Detection

Cited by 13 publications

References 33 publications

Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation

Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation

Hyperspectral Anomaly Detection via Dual Dictionaries Construction Guided by Two-Stage Complementary Decision

A joint model based on graph and deep learning for hyperspectral anomaly detection

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