Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

Zhao, Liaoying; Weijun, Lin; Wang, Yulei; Li, Xiaorun

doi:10.3390/rs10010103

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…One is the global RX (GRX), in which all pixels in the image are viewed as background and used to calculate background statistics. The other is the local RX (LRX) [20][21][22], in which a sliding fixed-size dual-window is employed to select those surrounding pixels as the background pixels of each tested pixel to calculate local background statistics. However, the multivariate Gaussian distribution is too simple to accurately characterize the complicated background of real-world hyperspectral scenes.…”

Section: Introductionmentioning

confidence: 99%

Spectra-Based Selective Searching for Hyperspectral Anomaly Detection

et al. 2020

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The research on hyperspectral anomaly detection algorithms has become a hotspot, driven by a lot of practical applications, such as mineral exploration, environmental monitoring and the national defense force. However, most existing hyperspectral anomaly detectors are designed with a single pixel as unit, which may not make full use of the spatial and spectral information in the hyperspectral image to detect anomalies. In this paper, to fully combine and utilize the spatial and spectral information of hyperspectral images, we propose a novel spectral-based selective searching method for hyperspectral anomaly detection, which firstly combines adjacent pixels with the same spectral characteristics into regions with adaptive shape and size and then treats those regions as one processing unit. Then, by fusing adjacent regions with similar spectral characteristics, the anomaly can be successfully distinguished from background. Two standard hyperspectral datasets are introduced to verify the feasibility and effectiveness of the proposed method. The detection performance is depicted by intuitive detection images, receiver operating characteristic curves and area under curve values. Comparing the results of the proposed method with five popular and state-of-the-art methods proves that the spectral-based selective searching method is an accurate and effective method to detect anomalies.

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Section: Introductionmentioning

confidence: 99%

Spectra-Based Selective Searching for Hyperspectral Anomaly Detection

et al. 2020

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“…It can collect a wide range of electromagnetic spectrum nearly from visible to long-wavelength infrared. These spectra are represented by hundreds of continuous bands that can meticulously describe the characteristics of different materials to recognize their subtle differences [3]. Therefore, owing to this good discriminative property of hyperspectral image, it has been widely used in many remote sensing research fields [4,5], such as image denoising [6,7], hyperspectral unmixing [8,9], band selection [10,11], target detection [12,13], and image classification [14,15].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

Yuan

Wang

2019

Remote Sensing

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A hyperspectral image usually covers a large scale of ground scene, which contains various materials with different spectral properties. When directly exploring the background information using all the image pixels, complex spectral interactions and inter-/intra-difference of different samples will significantly reduce the accuracy of background evaluation and further affect the detection performance. To address this problem, this paper proposes a novel hyperspectral anomaly detection method based on separability-aware sample cascade model. Through identifying separability of hyperspectral pixels, background samples are sifted out layer-by-layer according to their separable degrees from anomalies, which can ensure the accuracy and distinctiveness of background representation. First, as spatial structure is beneficial for recognizing target, a new spectral–spatial feature extraction technique is used in this work based on the PCA technique and edge-preserving filtering. Second, depending on different separability computed by sparse representation, samples are separated into different sets which can effectively and completely reflect various characteristics of background across all the cascade layers. Meanwhile, some potential abnormal targets are removed at each selection step to avoid their effects on subsequent layers. Finally, comprehensively taking different good properties of all the separability-aware layers into consideration, a simple multilayer anomaly detection strategy is adopted to obtain the final detection map. Extensive experimental results on five real-world hyperspectral images demonstrate our method’s superior performance. Compared with seven representative anomaly detection methods, our method improves the average detection accuracy with great advantages.

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“…Hyperspectral image (HSI) delivers rich spectral information [1] that usually covers a large spectral range almost from visible to mid-infrared. These spectra are divided into hundreds of approximately continuous and very narrow spectral bands which have a strong ability to precisely characterize different objects and accurately recognize the subtle differences between surface materials [2,3]. Benefiting from its high spectral resolution, hyperspectral image has been successfully used in many applications [4], such as target detection [5,6], image classification [7,8], band selection [9,10], and hyperspectral unmixing [11,12].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation

Yuan

Wang

2018

Remote Sensing

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Most hyperspectral anomaly detection methods directly utilize all the original spectra to recognize anomalies. However, the inherent characteristics of high spectral dimension and complex spectral correlation commonly make their detection performance unsatisfactory. Therefore, an effective feature extraction technique is necessary. To this end, this paper proposes a novel anomaly detection method via discriminative feature learning with multiple-dictionary sparse representation. Firstly, a new spectral feature selection framework based on sparse presentation is designed, which is closely guided by the anomaly detection task. Then, the representative spectra which can significantly enlarge anomaly's deviation from background are picked out by minimizing residues between background spectrum reconstruction error and anomaly spectrum recovery error. Finally, through comprehensively considering the virtues of different groups of representative features selected from multiple dictionaries, a global multiple-view detection strategy is presented to improve the detection accuracy. The proposed method is compared with ten state-of-the-art methods including LRX, SRD, CRD, LSMAD, RSAD, BACON, BACON-target, GRX, GKRX, and PCA-GRX on three real-world hyperspectral images. Corresponding to each competitor, it has the average detection performance improvement of about 9.9%,

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Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

Cited by 12 publications

References 22 publications

Spectra-Based Selective Searching for Hyperspectral Anomaly Detection

Spectra-Based Selective Searching for Hyperspectral Anomaly Detection

Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation

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