Detection of Multitransition Abrupt Changes in Multitemporal SAR Images

Dogan, Ozan; Perissin, Daniele

doi:10.1109/jstars.2013.2295357

Cited by 16 publications

(12 citation statements)

References 17 publications

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“…Consequently, various methods were adapted and used for CD techniques; those are reviewed in (Lu et al, 2004;Radke et al, 2005;Singh, 1989). Existing CD approaches are on the analogy of applied reasoning levels based on various strategies applied by algebraic (Maoguo Gong, Cao et al, 2012;Maoguo Gong et al, 2014;Singh & Talwar, 2014), transformation (Li & Yeh, 1998), classification (Dogan & Perissin, 2014;yousif & Ban, 2014), clustering (Maoguo Gong, Zhou et al, 2012;Shang et al, 2014), statistical methodssimilarity (Chesnokova & Erten, 2013;Inglada & Mercier, 2007) and dissimilarity, probabilistic techniques (Baselice et al, 2014;Hao et al, 2014;Wang et al, 2013;yousif & Ban, 2013), thresholding (Hongtao & Ban, 2014), contour techniques (Mura et al, 2008), fusion methods, machine learning (Bovolo et al, 2010;Celik, 2010;Vijaya Geetha & Kalaivani, 2018), techniques, etc., on the consideration of pixel-and objectbased change map (Hussain et al, 2013) learning by supervised, semi-supervised (Lal & Anouncia, 2015) and unsupervised approaches (Bazi et al, 2005;Bruzzone & Prieto, 2000). However, these approaches are identifying the differences in terms of pixels (Ma et al, 2012) or objects (Shang et al, 2014) to perform information related to single scale.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A feature based change detection approach using multi-scale orientation for multi-temporal SAR images

Geetha

Kalaivani

2020

European Journal of Remote Sensing

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Excellent operation regardless of weather conditions and superior resolution independent of sensor light are the most attractive and desired features of synthetic aperture radar (SAR) imagery. This paper proposes an exclusive multi-scale with multiple orientation approach for multi-temporal SAR images. This approach integrates pre-processing and change detection. Pre-processing is performed on the SAR imagery through speckle reducing anisotropic diffusion and discrete wavelet transform. The processed speckle-free images are designed by Log-Gabor filter bank in terms of multi-scale with multiple orientations. The maximum magnitude of multiple orientations is concatenated to obtain feature-based scale representation. Each scale is dealt with multiple orientations and is compared by band-wise subtraction to retrieve difference image (DI) coefficient. The series of the difference coefficients from each scale are add-on together to estimate a DI. Thus, the resultant image of multi-scale orientation gives perception of detailed information with specific contour. Constrained k-means clustering algorithm is preferred to achieve change and un-change map. Performance of the proposed approach is validated on three real SAR image datasets. The effective change detection is examined by using confusion matrix parameters. Experimental results are described to show the efficacy of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A feature based change detection approach using multi-scale orientation for multi-temporal SAR images

Geetha

Kalaivani

2020

European Journal of Remote Sensing

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“…A change map is likely to be easy to read, but is computed for changes only and prior information about the type of changes is required. For example, one may want to underline abrupt changes due to floods, earthquakes or anthropic activities (e.g., Inglada et al, 2003;Dogan and Perissin, 2014) while others may consider gradual changes such as biomass accumulation (e.g., Vina et al, 2004;Kayastha et al, 2012).…”

Section: Sits Analysis Approachesmentioning

confidence: 99%

Ranking evolution maps for Satellite Image Time Series exploration: application to crustal deformation and environmental monitoring

Méger

Rigotti

Pothier

et al. 2018

Data Min Knowl Disc

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Satellite Image Time Series (SITS) are large datasets containing spatiotemporal information about the surface of the Earth. In order to exploit the potential of such series, SITS analysis techniques have been designed for various applications such as earthquake monitoring, urban expansion assessment or glacier dynamic analysis. In this paper, we present an unsupervised technique for browsing SITS in preliminary explorations, before deciding whether to start deeper and more time consuming analyses. Such methods are lacking in today's analyst toolbox, especially when it comes to stimulating the reuse of the ever growing list

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“…The first approach concurrently compares pixels at all times in the same position by means of statistical hypothesis testing method called an omnibus test. A method that exploited the analysis of variance (ANOVA) model to detect abrupt changes in urban areas was presented by Dogan et al [21], which measured the relationship between and within pixel groups in the temporal dimension by testing the hypothesis of the means of all distributions being equal. Conradsen et al [20] performed a simultaneous test for the hypothesis of homogeneity of multitemporal polarimetric SAR data and derived a likelihood ratio test statistic based on the complex variance-covariance matrices to determine if at least one change happened in a time sequence.…”

Section: Introductionmentioning

confidence: 99%

Change Analysis in Urban Areas Based on Statistical Features and Temporal Clustering Using TerraSAR-X Time-Series Images

Yuan

Dou

et al. 2019

Remote Sensing

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The existing unsupervised multitemporal change detection approaches for synthetic aperture radar (SAR) images based on the pixel level usually suffer from the serious influence of speckle noise, and the classification accuracy of temporal change patterns is liable to be affected by the generation method of similarity matrices and the pre-specified cluster number. To address these issues, a novel time-series change detection method with high efficiency is proposed in this paper. Firstly, spatial feature extraction using local statistical information on patches is conducted to reduce the noise and for subsequent temporal grouping. Secondly, a density-based clustering method is adopted to categorize the pixel series in the temporal dimension, in view of its efficiency and robustness. Change detection and classification results are then obtained by a fast differential strategy in the final step. The experimental results and analysis of synthetic and realistic time-series SAR images acquired by TerraSAR-X in urban areas demonstrate the effectiveness of the proposed method, which outperforms other approaches in terms of both qualitative results and quantitative indices of macro F1-scores and micro F1-scores. Furthermore, we make the case that more temporal change information for buildings can be obtained, which includes when the first and last detected change occurred and the frequency of changes.

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Detection of Multitransition Abrupt Changes in Multitemporal SAR Images

Cited by 16 publications

References 17 publications

RETRACTED ARTICLE: A feature based change detection approach using multi-scale orientation for multi-temporal SAR images

RETRACTED ARTICLE: A feature based change detection approach using multi-scale orientation for multi-temporal SAR images

Ranking evolution maps for Satellite Image Time Series exploration: application to crustal deformation and environmental monitoring

Change Analysis in Urban Areas Based on Statistical Features and Temporal Clustering Using TerraSAR-X Time-Series Images

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