Adaptive Multitemporal SAR Image Filtering Based on the Change Detection Matrix

Lê, Thu Trang; Atto, Abdourrahmane M.; Trouvé, Emmanuël; Nicolas, Jean‐Marie

doi:10.1109/lgrs.2014.2311663

Cited by 37 publications

(7 citation statements)

References 23 publications

(23 reference statements)

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“…Several recent studies perform change detection via different deep learning ideas [29]- [33]. [34] and [35] employ approaches based on probabilistic distributions for error terms and change detection matrices. [36] and [37] propose change detection methods based on clustering.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Spatio-Temporal Change Detection on Multitemporal SAR Images

Fonseca

Negri²,

Pinheiro

et al. 2023

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

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We introduce the WECS (Wavelet Energies Correlation Screening), an unsupervised method to detect spatio-temporal changes on multitemporal SAR images. The procedure is based on wavelet approximation for the multitemporal images, wavelet energy apportionment, and ultra-high dimensional correlation screening for the wavelet coefficients. We show WECS's performance on simulated multitemporal image data. We also evaluate the proposed method on a time series of 85 Sentinel-1 images of a forest region at the border of Brazil and French Guiana. Comparisons with well-known change detection methods found in the literature highlight the proposal's superiority in terms of change detection accuracy. Additionally, the introduced method has simple architecture and low computational cost.

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Section: Introductionmentioning

confidence: 99%

Wavelet Spatio-Temporal Change Detection on Multitemporal SAR Images

Fonseca

Negri²,

Pinheiro

et al. 2023

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

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“…A large number of speckle filtering approaches have been proposed for multi-temporal SAR images [11][12][13][14][15][16]. Multi-temporal PolSAR image speckle filtering is seldom studied independently.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Speckle Filter for Multi-Temporal PolSAR Image with Multi-Dimensional Information Fusion

Cui

et al. 2023

Remote Sensing

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Polarimetric synthetic aperture radar (PolSAR) is an important sensor for earth observation. Multi-temporal PolSAR images obtained by successive observations of the region of interest contain rich polarimetric–temporal–spatial information of the land covers, which has wide applications. Speckle filtering becomes a necessary pre-processing for many subsequent applications. Currently, it is common to filter multi-temporal PolSAR data by directly using a speckle filter developed for single SAR or PolSAR data. The cross-correlation between different time series contains rich information in multi-temporal PolSAR images. How to utilize complete polarimetric–temporal–spatial information becomes a large challenge to achieve more satisfied performances of speckle reduction and details preservation simultaneously. This work dedicates to this issue and develops a novel speckle filtering approach for multi-temporal PolSAR data by multi-dimensional information fusion. The core idea is to establish an adaptive and efficient strategy of similar pixel selection based on the similarity test of multi-temporal polarimetric covariance matrices. This similar pixel selection scheme fuses the complete information of multi-temporal PolSAR data. The sensitivity of the proposed scheme is demonstrated with several typical and challenging texture patterns. Then, an adaptive speckle filter is established specifically for multi-temporal PolSAR data. Intensive comparison studies are carried out with airborne UAVSAR datasets and spaceborne ALOS/PALSAR datasets. Quantitative investigations in terms of the equivalent number of looks (ENL) and the figure of merit (FOM) indexes demonstrate and validate the superiority of the proposed method.

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“…Multi-temporal filters operate in both the spatial and temporal dimensions, thus the reduction in resolution is not as likely to be as severe. Previous research has shown that multi-temporal filtering is beneficial for several applications including monitoring of water intake volume in small reservoirs (Amitrano et al 2014), agriculture (Caves et al 2011), SAR classification (Bruzzone et al 2004;Hachicha et al 2011) and SAR change detection (Lê et al 2014). Multi-temporal filtering effectiveness is determined by the number of images in the stack and the size of the filtering window.…”

Section: Introductionmentioning

confidence: 99%

“…Multi-temporal filtering effectiveness is determined by the number of images in the stack and the size of the filtering window. There are many different multi-temporal filters which have been developed (Chierchia et al 2017;Deledalle et al 2019;Gineste 1999;Lê et al 2014;Su et al 2014;Yuan et al 2018;Zhao et al 2018). At the time of this analysis, only the GAMMA multi-temporal filter and the Multitemporal Lee filter were available in the GAMMA software, which was used to process the RADARSAT-2 imagery.…”

Section: Introductionmentioning

confidence: 99%

Temporal Filters for Mapping Phragmites with C-HH SAR Data

White

Brisco

Murnaghan

et al. 2020

Canadian Journal of Remote Sensing

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We compared traditional spatial filters and multi-temporal filters to remove speckle from synthetic aperture radar (SAR) data for mapping Phragmites australis. SAR constellations, with more rapid revisit capability, allow one to generate stacks of SAR data and to use multi-temporal filters for speckle reduction. GAMMA software offers multi-temporal filters for SAR processing, two of which we compared to the traditional Enhanced Lee and the Lee filters. We evaluated the filters using three criteria: (1) visual inspection, (2) signal level ratio, and (3) the equivalent number of looks (ENL). The results of this study show that multitemporal filters were able to reduce speckle from areas of surface water and land, as well as to improve the detection of Phragmites patches due to preserving the resolution and texture which helped in the detection of the patch boundaries. The signal level ratio was approximately 1.0 with the GAMMA Multi-temporal filter and approximately 0.9 with the other filters. The enhanced Lee and the two multi-temporal filters produced an equal ENL of about 6. However, due to small patch sizes and backscatter similarity with cattails and other cover types Phragmites patches were difficult to separate from other types of flooded vegetation with C-HH intensity only. RÉSUMÉNous avons compar e les filtres spatiaux traditionnels et les filtres multitemporels pour r eduire le chatoiement des donn ees de radars a synth ese d'ouverture (RSO) pour cartographier les Phragmites australis. Les constellations SAR, ayant une capacit e de revisite rapide, permettent de g en erer des piles de donn ees SAR et d'utiliser des filtres multitemporels pour la r eduction du bruit. Le logiciel GAMMA offre des filtres multitemporels pour le traitement SAR, dont deux que nous avons compar es aux filtres traditionnels Enhanced Lee et Lee. Nous avons evalu e les filtres a l'aide de trois crit eres: (1) inspection visuelle, (2) rapport du niveau de signal (SLR), et (3) le nombre equivalent de vues (ENL). Les r esultats montrent que les filtres multitemporels ont et e en mesure de r eduire le chatoiement des surfaces d'eau et des zones terrestres, ainsi que d'am eliorer la d etection des limites des plaques de phragmites en raison de la pr eservation de la r esolution et de la texture des images. Le rapport du niveau de signal etait d'environ 1,0 pour le filtre multitemporel GAMMA et d'environ 0,9 pour les autres filtres. Le filtre Lee am elior e et les deux filtres multitemporels ont produit un ENL semblable ($6). Cependant, en raison de la petite taille des parcelles et de la similitude de leur r etrodiffusion avec d'autres types de v eg etaux, les plaques de phragmites demeurent difficiles a s eparer des autres types de v eg etation inond ee en utilisant seulement l'intensit e de la bande C-HH.

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Adaptive Multitemporal SAR Image Filtering Based on the Change Detection Matrix

Cited by 37 publications

References 23 publications

Wavelet Spatio-Temporal Change Detection on Multitemporal SAR Images

Wavelet Spatio-Temporal Change Detection on Multitemporal SAR Images

Adaptive Speckle Filter for Multi-Temporal PolSAR Image with Multi-Dimensional Information Fusion

Temporal Filters for Mapping Phragmites with C-HH SAR Data

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