Entropy and multiscale analysis: a new feature extraction algorithm for aerial images

Winter, A.; Maître, H.; Cambou, N.; Legrand, E.

doi:10.1109/icassp.1997.595362

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…Research has been done on the use of scale space and entropy, but these investigations are based on the scale space of the histogram of the image [14,15], or the Gaussian pyramid [16]. Pluim et al [17,18] implemented a multi-scale approach to mutual information matching, aiming for an acceleration of the matching process while considering the accuracy and robustness of the method.…”

Section: Introductionmentioning

confidence: 99%

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Mutual information aspects of scale space images

Kuijper

2004

Pattern Recognition

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In image registration, mutual information is a well-performing measure based on principles of uncertainty. Similarly, in image analysis the Gaussian scale space, based on minimal assumptions of the image, is used to derive intrinsic properties of an image. This paper starts an investigation of a combination of both methods. This combination results in a double parameterized mutual information measure using local information of the image. For single modality matching best response is found for coinciding parameters. Then critical values are found for which the parameterized mutual information has extrema. First results on multi-modality matching show that different parameter values instead of coinciding values yield the best response for the parameterized mutual information.

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Section: Introductionmentioning

confidence: 99%

“…The reason for taking both kinds of images of the same patient is the need * Tel. : +45-38- 16 for determining a specific location of soft tissue (e.g. a brain tumor) in correspondence to the skull.…”

Section: Introductionmentioning

confidence: 99%

Mutual information aspects of scale space images

Kuijper

2004

Pattern Recognition

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“…Following the same manner as the estimation of GGD, a new random variable defined as has gamma distribution (22) Similarly, the ratio of the second moment to the square of the first moment of random variable is given as (23) Different from (6), both shape parameter and index shape parameter are involved in (23), which are needed to estimate. To remove index shape parameter from (23), it can be expressed by a scale independent function of shape parameter through the expectation and derivatives.…”

Section: B Generalized Gamma Distributionmentioning

confidence: 99%

“…In [22], several information theoretical similarity measures including distance to independence, mutual information, cluster reward algorithm, Woods criterion and correlation ratio, were compared for change detection, among which mutual information has been proved rather efficient. Taking the advantage of the mutual information, a pixel-based approach comparing the local mutual information shared by two pixels was proposed in [23]. Intuitively, when the two pixels share a lot of information, it is reasonable to assume no change happens in their locations.…”

Section: Introductionmentioning

confidence: 99%

Statistical Wavelet Subband Modeling for Multi-Temporal SAR Change Detection

Cui

Datcu

2012

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

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In the context of multi-temporal SAR change detection for earth monitoring applications, one critical issue is to generate accurate change map. A common method to generate change map is to apply logarithm to the ratio image. However, due to the speckle effect and without consideration of contextual information, it is usually not efficient for accurate change detection. In this paper, an unsupervised change detection method in wavelet domain based on statistical wavelet subband modeling is proposed. The motivation is to capture textures efficiently in wavelet domain. Wavelet transform is applied to decompose the image into multiple scales and probability density function of the coefficient magnitudes of each subband assumed to be Generalized Gaussian Distribution (GGD) and Generalized Gamma Distribution are obtained by fast parameter estimation. Closed-form expression of Kullback-Leibler divergence between two corresponding subbands of the same scale is computed and used to generate the change map. This approach is comprehensively evaluated and compared using different parameter setting, different scales, window sizes and estimators. The proposed SAR change detection in wavelet domain shows promising results as texture can be better characterized in wavelet domain than in spatial domain. Through this study, we conclude that the accuracy depends heavily on the estimation methods although the model is important. Both parameter estimation for GGD based on shape equation and parameter estimation for using method of log-cumulants (MoLC) in wavelet domain performs quite well. Index Terms-Generalized gamma distribution, Kullback-Leibler divergence, method of log-cumulants (MoLC), multitemporal SAR change detection, synthetic aperture radar (SAR), undecimated wavelet transform (UWT).

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“…Among them, the mutual information based similarity measure proved to be rather efficient. Taking advantage of the mutual information, a pixel-based approach comparing the localized mutual information shared by two pixels was proposed in [5]. Intuitively, when the two pixels share little mutual information, it is reasonable to assume change happens in their locations.…”

Section: Introductionmentioning

confidence: 99%

Information theoretical similarity measure for change detection

Cui

Datcu

Gueguen

2011

2011 Joint Urban Remote Sensing Event

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In this paper, mixed information similarity measure and a multidimensional density estimation method based on multivariate Edgeworth series expansion are proposed and assessed for the task of multi-temporal change detection. To unify mutual information and variational information, mixed information is proposed to quantify the degree of dependence between two random variables, which are intuitively appropriate for multi-temporal change detection. In the literature, Edgeworth series expansion is widely used in statistics and various engineering fields for one-dimensional density estimation. To compute the mixed information measure, multidimensional density estimation based on multivariate Edgeworth series expansion is proposed and evaluated. Two experiments on real SAR images and optical images are carried out to evaluate the performance of change detection. Experimental results confirm the promising capability of mixed information and the multivariate density estimation based on Edgeworth series expansion.

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Entropy and multiscale analysis: a new feature extraction algorithm for aerial images

Cited by 14 publications

References 9 publications

Mutual information aspects of scale space images

Mutual information aspects of scale space images

Statistical Wavelet Subband Modeling for Multi-Temporal SAR Change Detection

Information theoretical similarity measure for change detection

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