Hyperspectral Anomaly Detection via Background Estimation and Adaptive Weighted Sparse Representation

Zhu, Lingxiao; Wen, Gongjian

doi:10.3390/rs10020272

Cited by 43 publications

(12 citation statements)

References 42 publications

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“…For data L, p was first selected by FrFE maximization [29], where p = 0.2. The AUC values versus (W t , W b ) are listed in Table 4, and the optimal value was 0.9493 when (W t , W b ) were set to (9,11). Then, (W t , W b ) were set to (7,9) and Figure 17 shows the AUC values versus p. The optimal AUC value was 0.9529 and the corresponding optimal p was 1.…”

Section: Parameter Analysismentioning

confidence: 99%

“…The collaborative representation detector (CRD) [8] is based on the concept that background pixel points can be approximately represented by their spatial neighborhoods, while anomaly points cannot. Other AD algorithms based on sparse representations have been detailed in [9][10][11][12]. Another class of AD algorithms relies on particular statistical assumptions; these are considered traditional methods.…”

Section: Introductionmentioning

confidence: 99%

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Fractional Fourier Transform-Based Tensor RX for Hyperspectral Anomaly Detection

Zhang

Cheng

et al. 2022

Remote Sensing

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Anomaly targets in a hyperspectral image (HSI) are often multi-pixel, rather than single-pixel, objects. Therefore, algorithms using a test point vector may ignore the spatial characteristics of the test point. In addition, hyperspectral anomaly detection (AD) algorithms usually use original spectral signatures. In a fractional Fourier transform (FrFT), the signals in the fractional Fourier domain (FrFD) possess complementary characteristics of both the original reflectance spectrum and its Fourier transform. In this paper, a tensor RX (TRX) algorithm based on FrFT (FrFT-TRX) is proposed for hyperspectral AD. First, the fractional order of FrFT is selected by fractional Fourier entropy (FrFE) maximization. Then, the HSI is transformed into the FrFD by FrFT. Next, TRX is employed in the FrFD. Finally, according to the optimal spatial dimensions of the target and background tensors, the optimal AD result is achieved by adjusting the fractional order. TRX employs a test point tensor, making better use of the spatial characteristics of the test point. TRX in the FrFD exploits the complementary advantages of the intermediate domain to increase discrimination between the target and background. Six existing algorithms are used for comparison in order to verify the AD performance of the proposed FrFT-TRX over five real HSIs. The experimental results demonstrate the superiority of the proposed algorithm.

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Section: Parameter Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fractional Fourier Transform-Based Tensor RX for Hyperspectral Anomaly Detection

Zhang

Cheng

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In each box and for each class, the position of maximum A class was extracted and assigned the corresponding label, resulting in one label per class and box. Finally, as many background samples as target samples were randomly distributed outside all boxes to differentiate between target and non-target pixels [45,98,[106][107][108][109]. To balance the training dataset [110] an equal number of background samples as in each class was chosen [106,111,112].…”

Section: Training and Validation Boxesmentioning

confidence: 99%

Detecting Moving Trucks on Roads Using Sentinel-2 Data

et al. 2022

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In most countries, freight is predominantly transported by road cargo trucks. We present a new satellite remote sensing method for detecting moving trucks on roads using Sentinel-2 data. The method exploits a temporal sensing offset of the Sentinel-2 multispectral instrument, causing spatially and spectrally distorted signatures of moving objects. A random forest classifier was trained (overall accuracy: 84%) on visual-near-infrared-spectra of 2500 globally labelled targets. Based on the classification, the target objects were extracted using a developed recursive neighbourhood search. The speed and the heading of the objects were approximated. Detections were validated by employing 350 globally labelled target boxes (mean F1 score: 0.74). The lowest F1 score was achieved in Kenya (0.36), the highest in Poland (0.88). Furthermore, validated at 26 traffic count stations in Germany on in sum 390 dates, the truck detections correlate spatio-temporally with station figures (Pearson r-value: 0.82, RMSE: 43.7). Absolute counts were underestimated on 81% of the dates. The detection performance may differ by season and road condition. Hence, the method is only suitable for approximating the relative truck traffic abundance rather than providing accurate absolute counts. However, existing road cargo monitoring methods that rely on traffic count stations or very high resolution remote sensing data have limited global availability. The proposed moving truck detection method could fill this gap, particularly where other information on road cargo traffic are sparse by employing globally and freely available Sentinel-2 data. It is inferior to the accuracy and the temporal detail of station counts, but superior in terms of spatial coverage.

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“…A hyperspectral image (HSI) provides a rich source of spectral and spatial information about the materials in the scene [1], and it has been widely applied in many remote sensing areas [2][3][4], including classification [5][6][7][8][9], clustering [10][11][12], unmixing [13][14][15], image denoising [16,17], band selection [18,19], change detection [20], and target detection [21][22][23][24][25] or anomaly detection [26][27][28][29][30][31][32][33]. Among these applications, anomaly detection (AD) plays a significant role in military surveillance [34], agriculture [35], mineral exploration [36], environmental monitoring [34], maritime rescue [37], and so on [27,[38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Recursive RX with Extended Multi-Attribute Profiles for Hyperspectral Anomaly Detection

Yan

Ding

et al. 2023

Remote Sensing

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Hyperspectral anomaly detection (HAD) plays an important role in military and civilian applications and has attracted a lot of research. The well-known Reed–Xiaoli (RX) algorithm is the benchmark of HAD methods. Based on the RX model, many variants have been developed. However, most of them ignore the spatial characteristics of hyperspectral images (HSIs). In this paper, we combine the extended multi-attribute profiles (EMAP) and RX algorithm to propose the Recursive RX with Extended Multi-Attribute Profiles (RRXEMAP) algorithm. Firstly, EMAP is utilized to extract the spatial structure information of HSI. Then, a simple method of background purification is proposed. That is, the background is purified by utilizing the RX detector to remove the pixels that are more likely to be anomalies, which helps improve the ability of background estimation. In addition, a parameter is utilized to control the purification level and can be selected by experiments. Finally, the RX detector is used again between the EMAP feature and the new background distribution to judge the anomaly. Experimental results on six real hyperspectral datasets and a synthetic dataset demonstrate the effectiveness of the proposed RRXEMAP method and the importance of using the EMAP feature and background purity means. Especially, on the abu-airport-2 dataset, the AUC value obtained by the present method is 0.9858, which is higher than the second one, CRD, by 0.0198.

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Hyperspectral Anomaly Detection via Background Estimation and Adaptive Weighted Sparse Representation

Cited by 43 publications

References 42 publications

Fractional Fourier Transform-Based Tensor RX for Hyperspectral Anomaly Detection

Fractional Fourier Transform-Based Tensor RX for Hyperspectral Anomaly Detection

Detecting Moving Trucks on Roads Using Sentinel-2 Data

Recursive RX with Extended Multi-Attribute Profiles for Hyperspectral Anomaly Detection

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