Wavelet-based feature extraction from oceanographic images

Simhadri, K.K.; Iyengar, S. Sitharama; Holyer, Ronald J.; Lybanon, Matthew; Zachary, John

doi:10.1109/36.673670

Cited by 50 publications

(15 citation statements)

References 14 publications

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“…Wavelet transforms have been used in the remote sensing community for image blending (GarguetDuport et al, 1996;Zhou et al, 1998), for detection of haze (Du et al, 2002), spectral unmixing of hyperspectral data (Li, 2004), post-classification change detection (Raja, 2013), and feature extraction (Fukuda and Hirosawa, 1999;Niedermeier et al, 2000;Simhadri et al, 1998). In relation to vegetation dynamics, Sakamoto et al (2005) developed a method for detection of crop phenology incorporating wavelet filters.…”

Section: Wavelet Transforms In Remote Sensing and Forestry Applicationsmentioning

confidence: 99%

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Gómez¹,

White²,

Wulder³

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Wavelet Transforms In Remote Sensing and Forestry Applicationsmentioning

confidence: 99%

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Gómez¹,

White²,

Wulder³

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Algorithms that fall into this category may be further subdivided: distribution diversity (entropy) based methods (Vazquez et al 1999;Shimada et al 2005); histogram analysis Cornillon 1992, 1995;Marcello et al 2005); a clustering based approach (Holyer and Peckinpaugh 1989); and an examination of the moments combined with a-priori knowledge of the region (Gerson et al 1979;Coulter 1983). Other edge detectors tested in an oceanographic frontal context include the wavelet based approach of Simhadri et al (1998), mathematical morphology algorithms (Lea and Lybanon 1993;Krishnamurthy et al 1994) and the Ordered Structural Edge Detector of Holland and Yan (1992). The histogram based Single Image Edge Detector (SIED) of Cayula and Cornillon (1992) has been used in many studies: Ullman and Cornillon (2001), Hickox et al (2000) and Mavor and Bisagni (2001) to name but a few.…”

mentioning

confidence: 99%

A New Statistical Modeling Approach to Ocean Front Detection from SST Satellite Images

Hopkins

Challenor

Shaw

2010

Journal of Atmospheric and Oceanic Technology

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Ocean fronts are narrow zones of intense dynamic activity that play an important role in global ocean-atmosphere interactions. Owing to their highly variable nature, both in space and time, they are notoriously difficult features to adequately sample using traditional in-situ techniques. In this paper we propose a new statistical modelling approach to detecting and monitoring ocean fronts from AVHRR SST satellite images that builds on the 'front following' algorithm of Shaw and Vennell (2000). Weighted local likelihood is used to provide a smooth, non-parametric description of spatial variations in the position, mean temperature, width and temperature change of an individual front within an image. Weightings are provided by a Gaussian kernel function whose width is automatically determined by likelihood crossvalidation. The statistical model fitting approach allows estimation of the uncertainty of each parameter to be quantified, a capability not possessed by other techniques. The algorithm is shown to be robust to noise and missing data in an image, problems that hamper many of the existing front detection schemes. The approach is general and could be used with other remotely sensed data sets, model output or data assimilation products.1

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“…We present some of our earlier results [6]. The problems mentioned earlier led to the development of other multiresolution tools.…”

Section: Starck-murtagh-bijaoui Wavelet Transform [6]mentioning

confidence: 90%

“…This is the first step in a feature tracking problem. We expore a parallel computational technique to this problem defined in our earlier paper on feature detection problem [6].…”

Section: N000014-92-56003mentioning

confidence: 99%

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Efficient algorithm for feature extraction from oceanographic images

Iyengar

Simhadri²,

Trivedi³

Proceedings Fourth International Conference on High-Performance Computing

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This paper presents a n e w computational scheme based o n multiresolution decomposition for extracting the features of interest f r o m the oceanographic images by suppressing the noise. T h e multiresolution analysis f r o m the median presented by Starck-Murtagh-Bijaoui [4][5] is used for the noise suppression.A parallel approach is presented f o r this computationally intensive problem of infrared images.

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Wavelet-based feature extraction from oceanographic images

Cited by 50 publications

References 14 publications

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Historical forest biomass dynamics modelled with Landsat spectral trajectories

A New Statistical Modeling Approach to Ocean Front Detection from SST Satellite Images

Efficient algorithm for feature extraction from oceanographic images

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