Hyperspectral Change Detection in the Presenceof Diurnal and Seasonal Variations

Eismann, Michael T.; Meola, Joseph; Hardie, Russell C.

doi:10.1109/tgrs.2007.907973

Cited by 105 publications

(64 citation statements)

References 23 publications

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“…In particular, we have low-resolution single-band video [9], higher-resolution RGB video, and a short sequence of ten-band imagery that is obtained as the first ten principal components of what was initially 124-band hyperspectral imagery [5], [6].…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…A remarkably extensive change detection data set was acquired by Eismann et al [5], [6], using a 124-channel hyperspectral imager viewing the same scene over the course of many months [ Fig. 1(b)].…”

Section: Multispectral Image Sequencementioning

confidence: 99%

“…We also remark that the three detectors defined in Eqs. (5,6,11) are related by a simple expression:…”

mentioning

confidence: 99%

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Detection of ephemeral changes in sequences of images

Theiler

Adler‐Golden

2008

2008 37th IEEE Applied Imagery Pattern Recognition Workshop

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Abstract-The formalism of anomalous change detection, which was developed for finding unusual changes in pairs of images, is extended to sequences of more than two images. Extended algorithms based on RX, Chronochrome, and Hyper are presented for identifying the most anomalously changing pixels in a sequence of co-registered images. Experimental comparisons are performed both on real data with real anomalies and on real data with simulated anomalies.

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Section: Numerical Experimentsmentioning

confidence: 99%

Section: Multispectral Image Sequencementioning

confidence: 99%

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Detection of ephemeral changes in sequences of images

Theiler

Adler‐Golden

2008

2008 37th IEEE Applied Imagery Pattern Recognition Workshop

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“…Eismann et al [14] proposed an algorithm based on linear predictors to detect subtle targets against a complex background, and Kim [15] modified matched filtering using target signal exclusion. Song et al [16] proposed unsupervised changedetection algorithms using spectral unmixing and iterative error analysis (IEA).…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

et al. 2017

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Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth's surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

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“…These pervasive differences may be due to calibration, illumination, look angle, and even the choice of remote sensing platform. They can be caused by misregistration [4], [5], [6] of the images, or by diurnal and seasonal variations [7] in the scene. Becauses these differences are pervasive, their effects can be statistically characterized, just from the image pair.…”

Section: Introductionmentioning

confidence: 99%

Spatio-spectral anomalous change detection in hyperspectral imagery

Theiler

2013

2013 IEEE Global Conference on Signal and Information Processing

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Abstract-Because each pixel of a hyperspectral image contains so much information, many (successful) algorithms treat those pixels as independent samples, despite the evident spatial structure in the imagery. One way to exploit this structure is to incorporate spatial processing into pixel-wise anomalous change detection algorithms. But if this is done in the most straightforward way, a contaminated cross-covariance is produced. A spatial processing framework is proposed that avoids this contamination and enhances the performance of anomalous change detection algorithms in hyperspectral imagery.

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Hyperspectral Change Detection in the Presenceof Diurnal and Seasonal Variations

Cited by 105 publications

References 23 publications

Detection of ephemeral changes in sequences of images

Detection of ephemeral changes in sequences of images

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

Spatio-spectral anomalous change detection in hyperspectral imagery

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