Hyperspectral anomaly detection by local joint subspace process and support vector machine

Xiang, Pei; Zhou, Huixin; Li, Huan; Song, Shangzhen; Tan, Wei; Song, Jiangluqi; Gu, Lin

doi:10.1080/01431161.2019.1708504

Cited by 42 publications

(19 citation statements)

References 42 publications

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“…Artificial bee colony algorithm is a typical swarm intelligence algorithm, which can meet most of the optimization problems, but it is easy to fall into local extreme points in the later stage, the diversity of samples is reduced, and the search speed is slowed down [33].…”

Section: Proposed Methodsmentioning

confidence: 99%

Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

Zhao

Wang

et al. 2020

IEEE Access

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Artificial bee colony algorithm is an effective algorithm for parameter optimization, but the traditional artificial bee colony algorithm is liable to fall into local extreme points at a later stage. In this paper, we propose an improved artificial bee colony optimization algorithm, which solves the problems of premature convergence and falling into the local extreme value in the classification of hyperspectral images. First we use an improved chaotic sequence with higher randomness to initialize and update nectar sources to expand the distribution of nectar sources. Secondly, the optimized adaptive step size is introduced into the neighborhood search to speed up the algorithm convergence and improve the search efficiency. Then we build an improved artificial bee colony algorithm support vector machine optimization model to mine the optimal values of penalty factor C and kernel function parameter σ. Next, the model was used to perform classification experiments on two hyperspectral images (University of Pavia, Indian Pine) with different attributes, and compared with the traditional bee colony algorithm, genetic algorithm, and particle swarm algorithm. Experimental results on HSI datasets demonstrate the superiority of the proposed method over several well-known methods in both classification accuracy and convergence speed. INDEX TERMS Artificial bee colony, support vector machine, hyperspectral image, chaotic sequence.

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Section: Proposed Methodsmentioning

confidence: 99%

Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

Zhao

Wang

et al. 2020

IEEE Access

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“…To further suppress background and noise, an adaptive weight map is applied to the initial anomaly area map. In this section, the adaptive weight map is calculated on the original HSI by utilizing the spectral angular distance [41], thus the spectral information in HSI is fully considered.…”

Section: Adaptive Weightmentioning

confidence: 99%

Visual Attention and Background Subtraction With Adaptive Weight for Hyperspectral Anomaly Detection

Xiang

Song

Qin

et al. 2021

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

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Anomaly detection (AD) in hyperspectral target detection is of particular interest because no prior knowledge of ground object spectra is required. However, it is difficult to utilize the salient features of hyperspectral image (HSI) and mitigate the effects of noise in hyperspectral AD, which greatly limits the detection performance. Here we report a strategy to implement hyperspectral AD by visual attention model and background subtraction with adaptive weight. Through band selection method, the most discriminating bands are selected as the input images for subsequent processing. Then, the hyperspectral visual attention model (HVAM) is introduced, for the first time, into hyperspectral AD for extracting the salient feature map of the input images. Furthermore, the background subtraction process that can reduce the background and noise in the salient feature map is developed via curvature filter. Using this operation, the initial anomaly area map is obtained. Finally, incorporating with the spectral information, an adaptive weight map is applied to the initial anomaly area map to further suppress the background. In the experiment, the proposed method is compared with seven other state-of-the-art methods on synthetic and real-world HSI. Most importantly, the results demonstrate that the proposed method is effective and performs better than alternative methods. We believe that this method can open a new avenue of visual processing methods for hyperspectral AD. IndexTerms-Anomaly detection (AD), hyperspectral visual attention model (HVAM), background subtraction, curvature filter, hyperspectral image (HSI).

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“…The SVM is considered, along with the random forest classifier and artificial neural networks, one of the most effective supervised classification methods [38] for multidimensional, large-scale image data. The SVM method algorithms perform well on noisy data and small numbers of training pixels [38,39], are suitable for anomaly detection [40] and are usually more accurate than other classification algorithms [37,41].…”

Section: Fire Reference For Comparisonmentioning

confidence: 99%

Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

Amici

Piscini

2021

Remote Sensing

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Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by the ASI (Agenzia Spaziale Italiana, Italian Space Agency) mission launched in 2019 to measure the unique spectral features of diverse materials including vegetation and forest disturbances. In this study, we explored the potential use of this new sensor PRISMA for active wildfire characterization. We used the PRISMA hypercube acquired during the Australian bushfires of 2019 in New South Wales to test three detection techniques that take advantage of the unique spectral features of biomass burning in the spectral range measured by PRISMA. The three methods—the CO2-CIBR (continuum interpolated band ratio), HFDI (hyperspectral fire detection index) and AKBD (advanced K band difference)—were adapted to the PRISMA sensor’s characteristics and evaluated in terms of performance. Classification techniques based on machine learning algorithms (support vector machine, SVM) were used in combination with the visual interpretation of a panchromatic sharpened PRISMA image for validation. Preliminary analysis showed a good overall performance of the instrument in terms of radiance. We observed that the presence of the striping effect in the data can influence the performance of the indices. Both the CIBR and HFDI adapted for PRISMA were able to produce a detection rate spanning between 0.13561 and 0.81598 for CO2-CIBR and that between 0.36171 and 0.88431 depending on the chosen band combination. The potassium emission index turned out to be inadequate for locating flaming in our data, possibly due to multiple factors such as striping noise and the spectral resolution (12 nm) of the PRISMA band centered at the potassium emission.

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Hyperspectral anomaly detection by local joint subspace process and support vector machine

Cited by 42 publications

References 42 publications

Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

Visual Attention and Background Subtraction With Adaptive Weight for Hyperspectral Anomaly Detection

Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

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