Supervised High-Resolution Dual-Polarization SAR Image Classification by Finite Mixtures and Copulas

Krylov, Vladimir A.; Moser, Gabriele; Zerubia, Josiane

doi:10.1109/jstsp.2010.2103925

Cited by 65 publications

(44 citation statements)

References 47 publications

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“…Numerical experiments show large computational gain of up to 2 orders of magnitude. As future works, this method can be extended to remote sensing and computer vision [19], for instance by applying non-convex optimization to feature selection [20].…”

Section: Resultsmentioning

confidence: 99%

Active set strategy for high-dimensional non-convex sparse optimization problems

Boisbunon

Flamary

Rakotomamonjy

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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To cite this version:Aurélie Boisbunon, Rémi Flamary, Alain Rakotomamonjy. Active set strategy for high-dimensional non-convex sparse optimization problems. ABSTRACTThe use of non-convex sparse regularization has attracted much interest when estimating a very sparse model on high dimensional data. In this work we express the optimality conditions of the optimization problem for a large class of non-convex regularizers. From those conditions, we derive an efficient active set strategy that avoids the computing of unnecessary gradients. Numerical experiments on both generated and real life datasets show a clear gain in computational cost w.r.t. the state of the art when using our method to obtain very sparse solutions.

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Section: Resultsmentioning

confidence: 99%

Active set strategy for high-dimensional non-convex sparse optimization problems

Boisbunon

Flamary

Rakotomamonjy

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…This section presents the high resolution SAR image classification results of the proposed method called AML-CEM (Amplitude density mixtures of MnL with CEM), compared to the corresponding results obtained with other methods which are DSEM-MRF [22] and K-MnL. We have also tested supervised version of AML-CEM [10] where training and testing sets are determined by selecting some spatially disjoint class regions in the image, and we run the algorithm twice for training and testing.…”

Section: Simulation Resultsmentioning

confidence: 99%

Unsupervised Classification of SAR Images Using Hierarchical Agglomeration and EM

Kayabol

Krylov

Zerubia

2012

Lecture Notes in Computer Science

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Abstract. We implement an unsupervised classification algorithm for high resolution Synthetic Aperture Radar (SAR) images. The foundation of algorithm is based on Classification Expectation-Maximization (CEM). To get rid of two drawbacks of EM type algorithms, namely the initialization and the model order selection, we combine the CEM algorithm with the hierarchical agglomeration strategy and a model order selection criterion called Integrated Completed Likelihood (ICL). We exploit amplitude statistics in a Finite Mixture Model (FMM), and a Multinomial Logistic (MnL) latent class label model for a mixture density to obtain spatially smooth class segments. We test our algorithm on TerraSAR-X data.

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“…Furthermore, since shellfish beds can be patchy in nature, this may have consequences for the classification of beds using high-resolution data. Methods that take into account the variation within classes in combination with the non-Gaussian behavior of SAR information prior to classification [55] may be beneficial to further enhance shellfish mapping using SAR.…”

Section: Comparing Shellfish Maps From Sar With Traditional Field Surmentioning

confidence: 99%

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

Nieuwhof

Herman

Dankers³

et al. 2015

Remote Sensing

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On intertidal mudflats, reef-building shellfish, like the Pacific oyster and the blue mussel, provide a myriad of ecosystem services. Monitoring intertidal shellfish with high spatiotemporal resolution is important for fisheries, coastal management and ecosystem studies. Here, we explore the potential of X-(TerraSAR-X) and C-band (Radarsat-2) dual-polarized SAR data to map shellfish densities, species and coverage. We investigated two backscatter models (the integral equation model (IEM) and Oh's model) for inversion possibilities. Surface roughness (vertical roughness RMSz and correlation length L) was measured of bare sediments and shellfish beds, which was then linked to shellfish density, presence and species. Oysters, mussels and bare sediments differed in RMSz, but because the backscatter saturates at relatively low RMSz values, it was not possible to retrieve shellfish density or species composition from X-and C-band SAR. Using a classification based on univariate and multivariate logistic regression of the field and SAR image data, we constructed maps of shellfish presence (Kappa statistics for calibration 0.56-0.74 for dual-polarized SAR), which were compared with independent field surveys of the contours of the beds (Kappa statistics of agreement 0.29-0.53 when using dual-polarized SAR). We conclude that spaceborne SAR allows one to monitor the contours of shellfish-beds (thus, distinguishing shellfish substrates from bare sediment and dispersed single shellfish), but not densities and species. Although OPEN ACCESSRemote Sens. 2015, 7 3711 spaceborne SAR cannot replace ground surveys entirely, it could very well offer a significant improvement in efficiency.

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Supervised High-Resolution Dual-Polarization SAR Image Classification by Finite Mixtures and Copulas

Cited by 65 publications

References 47 publications

Active set strategy for high-dimensional non-convex sparse optimization problems

Active set strategy for high-dimensional non-convex sparse optimization problems

Unsupervised Classification of SAR Images Using Hierarchical Agglomeration and EM

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

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