Sparsity-Driven Change Detection in Multitemporal SAR Images

Nar, Fatih; Özgür, Atilla; Saran, Ayse Nurdan

doi:10.1109/lgrs.2016.2562032

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…As can be seen from Table 1, numerous approaches exist for finding weights in ensemble classification. Inspired from studies of [16,21,30,33,39], sparsity-driven weighted ensemble classifier (SDWEC) has been proposed. SDWEC can use both strong classifiers or weak classifiers for classifier ensemble.…”

Section: Related Work: Ensembles That Combine Pre-trained Classifiersmentioning

confidence: 99%

“…The second term is a sparsity term [40] that forces weights to be sparse [39]; therefore, minimum number of classifiers are utilized. In sparsity term, any L p -norm (0 p 1) can be used.…”

Section: Sparsity-driven Weighted Ensemble Classifiermentioning

confidence: 99%

“…Inspired from mentioned studies [16,20,21] and authors' earlier work [23], sparsity-driven weighted ensemble classifier (SDWEC) has been proposed. Our proposed cost function and solution differs from previous studies.…”

Section: Introductionmentioning

confidence: 99%

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Sparsity-driven weighted ensemble classifier

Özgür

Erdem²,

Nar³

2018

IJCIS

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In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affect classifier accuracy. In the proposed method, ensemble weights finding problem is modeled as a cost function with the following terms: (a) a data fidelity term aiming to decrease misclassification rate, (b) a sparsity term aiming to decrease the number of classifiers, and (c) a non-negativity constraint on the weights of the classifiers. As the proposed cost function is non-convex thus hard to solve, convex relaxation techniques and novel approximations are employed to obtain a numerically efficient solution. Sparsity term of cost function allows trade-off between accuracy and testing time when needed. The efficiency of SDWEC was tested on 11 datasets and compared with the state-of-the art classifier ensemble methods. The results show that SDWEC provides better or similar accuracy levels using fewer classifiers and reduces testing time for ensemble.

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Section: Related Work: Ensembles That Combine Pre-trained Classifiersmentioning

confidence: 99%

“…The second term is a sparsity term [40] that forces weights to be sparse [39]; therefore, minimum number of classifiers are utilized. In sparsity term, any L p -norm (0 p 1) can be used.…”

Section: Sparsity-driven Weighted Ensemble Classifiermentioning

confidence: 99%

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Sparsity-driven weighted ensemble classifier

Özgür

Erdem²,

Nar³

2018

IJCIS

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“…functions are non-differentiable which makes the minimization difficult. Inspired from [13,14], we setf p as a proxy for f p to be able to approximate non-differentiable terms in equations (4), (5), and (6). First, the absolute function in the data fidelity term is approximated as below:…”

Section: Although Equation (3) Is Convex Absolute and Maxmentioning

confidence: 99%

“…where sgn is the sign function. The approximated cost function in equation ( 7) is accurate around fp so it must be solved in an iterative manner [14], where n is the iteration number. This cost function has a different data fidelity term and numerical minimization approach and it is also iterative comparing to two-phase solution proposed in [4].…”

Section: Minimization Of the Cost Functionmentioning

confidence: 99%

Sparsity-Driven Digital Terrain Model Extraction

Nar

Yilmaz²,

Camps‐Valls

2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

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We here introduce an automatic Digital Terrain Model (DTM) extraction method. The proposed sparsitydriven DTM extractor (SD-DTM) takes a high-resolution Digital Surface Model (DSM) as an input and constructs a high-resolution DTM using the variational framework. To obtain an accurate DTM, an iterative approach is proposed for the minimization of the target variational cost function. Accuracy of the SD-DTM is shown in a real-world DSM data set. We show the efficiency and effectiveness of the approach both visually and quantitatively via residual plots in illustrative terrain types. Index Termsdigital surface model, digital terrain model, sparsity, variational inference 1. INTRODUCTION A Digital Terrain Model (DTM) is an elevation map of bare ground where man-made objects (buildings, vehicles, etc.) as well as vegetation (trees, bushes, etc.) are removed from the Digital Surface Model (DSM) [1]. In

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