Application of log-cumulants to change detection on multi-temporal SAR images

Bujor, F.; Nicolas, Jean‐Marie; Trouvé, E.; Rudant, Jean-Paul

doi:10.1109/igarss.2003.1294117

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…For SAR images, the ratio operator is often used to generate the change image since it will eliminate the speckle noise assuming this noise is multiplicative and reproduced in repeatpass images [12]. In [14], the authors found the ratio operator to be more suitable than second and third cumulants when it comes to detecting sharp changes such as those associated with urban areas. In [24], a neighborhood-based ratio operator was developed in order to further reduce the effect of speckle thus improve change detection.…”

Section: B Unsupervised Change Detectionmentioning

confidence: 98%

“…Among them, minimum error thresholding is the most commonly used unsupervised change detection algorithm [21]. This algorithm has been applied successfully to both multitemporal optical data [21] and multitemporal SAR images after introducing some modifications [13] and [14].…”

Section: B Unsupervised Change Detectionmentioning

confidence: 99%

“…For examples, [12] and [13] performed unsupervised change detection using a generalized Kittler-Illingworth minimum error thresholding algorithm to account for the non-Gaussian distribution of SAR images. [14] investigated four different types of change variables computed from multitemporal SAR images for the purpose of change detection. [15] combined feature-based and pixel-based techniques for change detection in urban areas and found the method to be effective, irrespectively of misregistration errors due to reprojection problems or difference in the sensor's viewing geometry, which are common in multitemporal SAR images.…”

Section: Introductionmentioning

confidence: 99%

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Multitemporal Spaceborne SAR Data for Urban Change Detection in China

Ban

Yousif

2012

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

142

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The objective of this research is to examine effective methods for urban change detection using multitemporal spaceborne SAR data in two rapid expanding cities in China. One scene of ERS-2 SAR C-VV image was acquired in Beijing in 1998 and in shanghai in 1999 respectively and one scene of ENVISAT ASAR C-VV image was acquired in near-anniversary dates in 2008 in Beijing and Shanghai. To compare the SAR images from different dates, a modified ratio operator that takes into account both positive and negative changes was developed to derive a change image. A generalized version of Kittler-Illingworth minimum-error thresholding algorithm was then tested to automatically classify the change image into two classes, change and no change. Various probability density functions such as Log normal, Generalized Gaussian, Nakagami ratio, and Weibull ratio were investigated to model the distribution of the change and no change classes. The results showed that Kittler-Illingworth algorithm applied to the modified ratio image is very effective in detecting temporal changes in urban areas using SAR images. Log normal and Nakagami density models achieved the best results. The Kappa coefficients of these methods were of 0.82 and 0.71 for Beijing and Shanghai respectively while the false alarm rates were 2.7% and 4.75%. The findings indicated that the change accuracies obtained using Kittler-Illingworth algorithm vary depending on how the assumed conditional class density function fits the histograms of change and no change classes.

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Section: B Unsupervised Change Detectionmentioning

confidence: 98%

Section: B Unsupervised Change Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multitemporal Spaceborne SAR Data for Urban Change Detection in China

Ban

Yousif

2012

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

142

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“…For SAR images, the ratio operator is often used to generate the change image since it will eliminate the speckle noise assuming this noise is multiplicative and reproduced in repeat-pass images. [7] found the ratio operator more suitable when it comes to detecting sharp changes such as those associated with urban areas. In this study a modified version of the ratio operator was used to generate the change image.…”

Section: ) Problem Formulationmentioning

confidence: 98%

Unsupervised change detection using multitemporal spaceborne SAR data: A case study in Beijing

Ban

Yousif

2011

2011 Joint Urban Remote Sensing Event

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The objective of this research is to examine unsupervised change detection methods using multitemporal spaceborne SAR data for urbanization monitoring in Beijing. One scene of ENVISAT ASAR C-VV image was acquired in July, 2008 and one scene of ERS-2 SAR C-VV image was acquired in July, 1998. To compare the two SAR images, a modified ratio operator that takes into account both positive and negative changes was developed to derive a change image. A generalized version of Kittler-Illingworth minimum-error thresholding algorithm was then tested to automatically classify the change image into two classes, change and no-change. Various probability density functions such as Log normal, Generalized Gaussian, Nakagami ratio, and Weibull ratio were investigated to model the distribution of the change and no-change classes. The preliminary results showed that Kittler-Illingworth algorithm applied to the modified ratio image is very effective in detecting temporal changes in urban areas using SAR images. Log normal and Nakagami density models achieved the best results. The Kappa coefficients of the these solutions were of 0.82 while the false alarm rates were 2.7%. The initial findings indicated that the accuracy of the change result obtained using Kittler-Illingworth algorithm varies depending on how the assumed conditional class density function fits the histograms of change and no-change classes.

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“…In particular, they used the ratio operator to compare the temporal images and a decision mechanism based on a desired value of the probability of false alarms. In [19], four different features (the temporal ratio, second-, and third-order Log-cumulants, and a mixture parameter of two Gamma distributions) are extracted from a series of seven multitemporal SAR images and studied for change-detection purposes. The unsupervised discrimination between changed and unchanged classes was carried out using the K-means clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images

Bazi

Bruzzone

Melgani

2005

IEEE Trans. Geosci. Remote Sensing

627

333

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Abstract-In this paper, we present a novel automatic and unsupervised change-detection approach specifically oriented to the analysis of multitemporal single-channel single-polarization synthetic aperture radar (SAR) images. This approach is based on a closed-loop process made up of three main steps: 1) a novel preprocessing based on a controlled adaptive iterative filtering; 2) a comparison between multitemporal images carried out according to a standard log-ratio operator; and 3) a novel approach to the automatic analysis of the log-ratio image for generating the change-detection map. The first step aims at reducing the speckle noise in a controlled way in order to maximize the discrimination capability between changed and unchanged classes. In the second step, the two filtered multitemporal images are compared to generate a log-ratio image that contains explicit information on changed areas. The third step produces the change-detection map according to a thresholding procedure based on a reformulation of the Kittler-Illingworth (KI) threshold selection criterion. In particular, the modified KI criterion is derived under the generalized Gaussian assumption for modeling the distributions of changed and unchanged classes. This parametric model was chosen because it is capable of better fitting the conditional densities of classes in the log-ratio image. In order to control the filtering step and, accordingly, the effects of the filtering process on change-detection accuracy, we propose to identify automatically the optimal number of despeckling filter iterations [Step 1)] by analyzing the behavior of the modified KI criterion. This results in a completely automatic and self-consistent change-detection approach that avoids the use of empirical methods for the selection of the best number of filtering iterations. Experiments carried out on two sets of multitemporal images (characterized by different levels of speckle noise) acquired by the European Remote Sensing 2 satellite SAR sensor confirm the effectiveness of the proposed unsupervised approach, which results in change-detection accuracies very similar to those that can be achieved by a manual supervised thresholding.

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Application of log-cumulants to change detection on multi-temporal SAR images

Cited by 7 publications

References 4 publications

Multitemporal Spaceborne SAR Data for Urban Change Detection in China

Multitemporal Spaceborne SAR Data for Urban Change Detection in China

Unsupervised change detection using multitemporal spaceborne SAR data: A case study in Beijing

An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images

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