A Comparison Between Coherent and Incoherent Similarity Measures in Terms of Crop Inventory

Chesnokova, Olga; Erten, Esra

doi:10.1109/lgrs.2012.2203783

Cited by 5 publications

(2 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RETRACTED ARTICLE: A feature based change detection approach using multi-scale orientation for multi-temporal SAR images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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“…The DI is achieved by comparing two co-registered images by various mathematical functions. In the literature, many comparison methods (Dekker, 1998;Lu, Mausel, Brondizio, & Moran, 2004;Singh, 1989;yousif & Ban, 2014) are available to find the DI such as algebra-based approach (Masroor, Dongmei, Angela, Hui, & David, 2013), transformation-based approach (Li & Yeh, 1998), classification-based approach (Jesus, Arie, & Joost, 2012) and similarity measures (Chesnokova & Erten, 2013). Out of these methods, algebra-based approach is a simple method to find DI which utilizes image differencing (Ma et al, 2012), image rationing, image regression, image correlation and change vector analysis (Sartajvir & Rajneesh, 2014).…”

Section: Introductionmentioning

confidence: 99%

Laplacian pyramid-based change detection in multitemporal SAR images

Geetha

Kalaivani

2019

European Journal of Remote Sensing

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The Synthetic Aperture Radar (SAR) has many attractive and desired features to cope with uncertain weather conditions and also provides superlative resolution in images. In this paper, a unique multiscale approach of change detection (CD) that integrates the preprocessing and CD technique of SAR imagery is discussed. Condensed anisotropic diffusion method is employed as a preprocessing tool for despeckling, and the resultant image is processed by multiscale approach in the Laplacian pyramid (LP) domain. In each layer of LP, the difference between the pyramid coefficient of two images of the same area obtained at different periods is estimated through log ratio operation. Each layer of pyramid coefficient has explicit features at particular scale. Fine scale related to a band pass layer localizes edges of the change area and the coarse scale related to a low pass layer preserves more detail information of the change region. Reconstruction of LP provides exact difference image with perfect boundary. Otsu's algorithm is used to generate binary change map to distinguish changed and unchanged pixels. The efficiency of the proposed method is tested by using simulated and real SAR image datasets. The confusion matrix parameters are used to prove competence of the proposed CD method.

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Paddy-Rice Phenology Classification Based on Machine-Learning Methods Using Multitemporal Co-Polar X-Band SAR Images

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Taşkın

Erten

2016

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

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A Comparison Between Coherent and Incoherent Similarity Measures in Terms of Crop Inventory

Cited by 5 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

Laplacian pyramid-based change detection in multitemporal SAR images

Paddy-Rice Phenology Classification Based on Machine-Learning Methods Using Multitemporal Co-Polar X-Band SAR Images

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