Subpixel Anomalous Change Detection in Remote Sensing Imagery

Theiler, James

doi:10.1109/ssiai.2008.4512311

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…ACE computes the cosine between the reference signature and the test pixel vectors in the hyperspace whitened by means of the background covariance matrix. As to the ACD task, among the different approaches presented in the literature 16,17 , we have adopted the one proposed in a recently published paper 18 that is based on spatially local subspaces estimation and projections.…”

Section: Methods Descriptionmentioning

confidence: 99%

Combining spectral matching and anomalous change detection for target rediscovery in hyperspectral images

Rossi

Acito

Diani

et al. 2014

Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; And Military Applications in Hyperspectral Imagin

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In surveillance applications, tracking a specific target by means of subsequent acquisitions over the monitored area is of great interest. Multitemporal HyperSpectral Images (HSIs) are particularly suitable for this application. Multiple HSIs of the same scene collected at different times can be exploited to detect changes using anomalous change detection (ACD) techniques. Moreover, spectral matching (SM) is a valuable tool for detecting the target spectrum within HSIs collected at different times (target rediscovery-TR). Depending on the monitored area and the specific target of interest, TR can be a challenging task. In fact, it may happen that the target has spectral features similar to those of uninteresting objects in the scene and the use of SM techniques without additional information can generate too many misleading detections. We introduce a new TR strategy aimed at mitigating the number of alarms encountered in complex scenarios. The proposed detection strategy combines the SM approach with the unsupervised ACD strategy. We focus on rediscovery of moving targets in airborne HSIs collected on the same complex area. False alarms mitigation is achieved by exploiting both the target spectral features and the temporal variations of its position. For this purpose, SM is performed only on those pixels that have undergone changes within multiple acquisitions. Results obtained applying the proposed scheme on real HSIs are presented and discussed. The results show the effectiveness of the fusion of spectral and multitemporal analysis to improve TR performance in complex scenarios

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Section: Methods Descriptionmentioning

confidence: 99%

Combining spectral matching and anomalous change detection for target rediscovery in hyperspectral images

Rossi

Acito

Diani

et al. 2014

Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; And Military Applications in Hyperspectral Imagin

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“…Thus, the eigenvectors of Q RX and Q HACD are identical, and the eigenvalues differ by 1. Similarly, the coefficient matrix of the Subpixel Hyperbolic method [9] …”

Section: Matrixmentioning

confidence: 99%

“…A number of ACD algorithms can be expressed as quadratic functions of the data, where the coefficients are based on the covariances and cross-covariances of two images [2] being compared. Among these methods are the RX [3], Chronochrome [4], Whitened Total Least Squares (WTLSQ) [5], Covariance Equalization [6], Multivariate Alteration Detection [7], Hyperbolic [8] and Subpixel Hyperbolic [9] methods. The eigenvalue spectrum of coefficient matrices can provide valuable insights into the algebraic and numerical properties of the covariance-based quadratic ACD methods.…”

Section: Introductionmentioning

confidence: 99%

Block-diagonal representations for covariance-based Anomalous change detectors

Matsekh

Theiler

2010

2010 IEEE International Geoscience and Remote Sensing Symposium

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We use singular vectors of the whitened cross-covariance matrix of two hyper-spectral images and the Golub-Kahan permutations in order to obtain equivalent tridiagonal representations of the coefficient matrices for a family of covariancebased quadratic Anomalous Change Detection (ACD) algorithms. Due to the nature of the problem these tridiagonal matrices have block-diagonal structure, which we exploit to derive analytical expressions for the eigenvalues of the coefficient matrices in terms of the singular values of the whitened cross-covariance matrix. The block-diagonal structure of the matrices of the RX, Chronochrome, symmetrized Chronochrome, Whitened Total Least Squares, Hyperbolic and Subpixel Hyperbolic Anomalous change detectors are revealed by the white singular value decomposition and GolubKahan transformations. Similarities and differences in the properties of these change detectors are illuminated by their eigenvalue spectra.

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Detection of Large-Scale and Anomalous Changes

Ziemann

Matteoli

2020

Hyperspectral Image Analysis

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Subpixel Anomalous Change Detection in Remote Sensing Imagery

Cited by 11 publications

References 10 publications

Combining spectral matching and anomalous change detection for target rediscovery in hyperspectral images

Combining spectral matching and anomalous change detection for target rediscovery in hyperspectral images

Block-diagonal representations for covariance-based Anomalous change detectors

Detection of Large-Scale and Anomalous Changes

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