Automatic Shoreline Detection and Change Detection Analysis of Netravati-GurpurRivermouth Using Histogram Equalization and Adaptive Thresholding Techniques

Aedla, Raju; Dwarakish, G. S.; Reddy, D. V.

doi:10.1016/j.aqpro.2015.02.073

Cited by 98 publications

(38 citation statements)

References 33 publications

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“…Aedla et al [108] proposed a method using adaptive thresholding. Other authors, like Kuleli, [109], used Otsu's method but in association with classification techniques.…”

Section: Thesholdingmentioning

confidence: 99%

Shoreline Detection using Optical Remote Sensing: A Review

Toure

Diop

Kpalma

et al. 2019

IJGI

131

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With coastal erosion and the increased interest in beach monitoring, there is a greater need for evaluation of the shoreline detection methods. Some studies have been conducted to produce state of the art reviews on shoreline definition and detection. It should be noted that with the development of remote sensing, shoreline detection is mainly achieved by image processing. Thus, it is important to evaluate the different image processing approaches used for shoreline detection. This paper presents a state of the art review on image processing methods used for shoreline detection in remote sensing. It starts with a review of different key concepts that can be used for shoreline detection. Then, the applied fundamental image processing methods are shown before a comparative analysis of these methods. A significant outcome of this study will provide practical insights into shoreline detection.

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“…Aedla et al [108] proposed a method using adaptive thresholding. Other authors, like Kuleli, [109], used Otsu's method but in association with classification techniques.…”

Section: Thesholdingmentioning

confidence: 99%

Shoreline Detection using Optical Remote Sensing: A Review

Toure

Diop

Kpalma

et al. 2019

IJGI

131

View full text Add to dashboard Cite

show abstract

“…Aktas et al (2012) presented a similar approach which used a different merging method integrating intensity differences and edge information. Aedla et al (2015) modified the histogram of the NIR image according to the mean plateau value to erase local maximum values. The automatic threshold was decided by calculating the mean and variance of the equalized histogram.…”

Section: Introductionmentioning

confidence: 99%

Efficient sea–land segmentation using seeds learning and edge directed graph cut

et al. 2016

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“…The coastline, the boundary of land and sea, is one of the 27 most important land surface features, and is vulnerable to natural processes such as coastal erosion/accretion, sea level changes and human activities [1]. Coastline mapping is, therefore, becoming a fundamental work for coastal erosion monitoring, coastal resource management, coastal environmental protection and coastal sustainable development [2][3][4][5][6]. In reality, the shoreline accurate position is difficult to be localized, as the position changes continually through time, because of cross-shore and alongshore sediment movement in the littoral zone and especially because of the dynamic nature of water levels at the coastal boundary (e.g., waves, tides, groundwater, storm surges, setups, runups, etc.)…”

Section: Introductionmentioning

confidence: 99%

Automatic Semi-Global Artificial Shoreline Subpixel Localization Algorithm for Landsat Imagery

et al. 2019

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Shoreline mapping using satellite remote sensing images has the advantages of large-scale surveys and high efficiency. However, low spatial resolution, various geometric morphologies and complex offshore environments prevent accurate positioning of the shoreline. This article proposes a semi-global subpixel shoreline localization method that considers utilizing morphological control points to divide the initial artificial shoreline into segments of relatively simple morphology and analyzing the local intensity homogeneity to calculate the intensity integral error. Combined with the segmentation-merge-fitting method, the algorithm determines the subpixel location accurately. In experiments, we select five artificial shorelines with various geometric morphologies from Landsat 8 Operational Land Imager (OLI) data. The five subpixel artificial shoreline RMSE results lie in the range of 3.02 m to 4.77 m, with line matching results varying from 2.51 m to 3.72 m. Thus, it can be concluded that the proposed subpixel localization algorithm is effective and applicable to artificial shoreline in various geometric morphologies and is robust to complex offshore environments, to some extent.

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Automatic Shoreline Detection and Change Detection Analysis of Netravati-GurpurRivermouth Using Histogram Equalization and Adaptive Thresholding Techniques

Cited by 98 publications

References 33 publications

Shoreline Detection using Optical Remote Sensing: A Review

Shoreline Detection using Optical Remote Sensing: A Review

Efficient sea–land segmentation using seeds learning and edge directed graph cut

Automatic Semi-Global Artificial Shoreline Subpixel Localization Algorithm for Landsat Imagery

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