Fast Hyperspectral Anomaly Detection via SVDD

Banerjee, Amit; Burlina, Philippe; Meth, Reuven

doi:10.1109/icip.2007.4379964

Cited by 47 publications

(31 citation statements)

References 10 publications

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“…The SVDD has been successfully applied in a wide variety of application domains, such as handwritten digit recognition [23], facial recognition [24], and anomaly detection [25,26].…”

Section: Support Vector Data Descriptionmentioning

confidence: 99%

Deep learning with support vector data description

2015

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“…The SVDD has been successfully applied in a wide variety of application domains, such as handwritten digit recognition [23], facial recognition [24], and anomaly detection [25,26].…”

Section: Support Vector Data Descriptionmentioning

confidence: 99%

Deep learning with support vector data description

2015

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“…Among others, airborne detection of landmines is one of the applications of an anomaly detector. The conventional RX discussed here is the baseline reference in this research field, and more powerful alternatives exist such as Support Vector methods [26] or Kernel RX [27]. The objective of this example is to provide some insight into how the strategies presented throughout this article can be extended to other fields of interest of the geophysics community in addition to image coding; not to improve the state of the art on said fields, which would be the object of another article.…”

Section: Hyperspectral Image Processing Example For Anomaly Detectionmentioning

confidence: 99%

Divide-and-Conquer Strategies for Hyperspectral Image Processing: A Review of Their Benefits and Advantages

Blanes

Serra-Sagristà²,

Marcellin

et al. 2012

IEEE Signal Process. Mag.

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“…(2) The Support Vector approach, SVDD, is a non-parametric method with several advantages, including a non-Gaussian modeling basis that can model arbitrarily shaped and multimodal distributions, scarcity and high generalization ability [44]. The SVDD approach does not require a covariance matrix inverse computation and is linear with respect to spectral dimensionality [44]. A processed SVDD with kernel statistics is expressed in Equation (7).…”

Section: Unmixing and Anomaly Detectionmentioning

confidence: 99%

“…However, the number of required components is not known, so in practice the background model is inaccurately estimated, resulting in poor detection performance (Figure 11(B)). (2) The Support Vector approach, SVDD, is a non-parametric method with several advantages, including a non-Gaussian modeling basis that can model arbitrarily shaped and multimodal distributions, scarcity and high generalization ability [44]. The SVDD approach does not require a covariance matrix inverse computation and is linear with respect to spectral dimensionality [44].…”

Section: Unmixing and Anomaly Detectionmentioning

confidence: 99%

Advantages of the Boresight Effect in Hyperspectral Data Analysis

Brook

Ben‐Dor

2011

Remote Sensing

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Dual pushbroom hyperspectral sensors consist of two different instruments (covering different wavelengths) that are usually mounted on the same optical bench. This configuration leads to problems, such as co-registration of pixels and squint of the field of view, known as the boresight effect. Determination of image-orientation parameters is due to the combination of an inertial measurement system (IMU) and global position system (GPS). The different positions of the IMU, the GPS antenna and the imaging sensors cause the orientation and boresight effect. Any small change in the correction of the internal orientation affects the co-registration between images extracted from the two instruments. Correcting the boresight effect is a key and almost automatic task performed by all dual-system users to better analyze the full spectral information of a given pixel. Thus, the boresight effect is considered to be noise in the system and a problem that needs to be corrected prior to any (thematic) data analysis. We propose using the boresight effect, prior to its correction, as a tool to monitor and detect spectral phenomena that can provide additional information not present in the corrected images. The advantage of using this effect was investigated with the AISA-Dual sensor, composed of an EAGLE sensor for the VIS-NIR (VNIR) region (400-970 nm) and HAWK for the SWIR region (980-2,450 nm). During the course of more than six years of operating this sensor, we have found that the boresight effect provides a new capacity to analyze hyperspectral data, reported herein. Accordingly, we generated a protocol to use this effect for three applications: (1) enhancing the shadow effect; (2) generating a 3-D view; and (3) better detecting OPEN ACCESSRemote Sensing 2011, 3 485 spectral/spatial anomalies based on sub-pixel edge detection. This paper provides examples of these applications and suggests possible uses from an airborne platform.

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Fast Hyperspectral Anomaly Detection via SVDD

Cited by 47 publications

References 10 publications

Deep learning with support vector data description

Deep learning with support vector data description

Divide-and-Conquer Strategies for Hyperspectral Image Processing: A Review of Their Benefits and Advantages

Advantages of the Boresight Effect in Hyperspectral Data Analysis

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