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

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Automatic Shoreline Detection and Change Detection Analysis of Netravati-Gurpurrivermouth Using Histogram Equalization and Adaptive Thresholding Techniques Available online at www.sciencedirect.com ScienceDirect Aquatic Procedia 4 ( 2015 ) 563 – 570 INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015) Automatic Shoreline Detection and Change Detection Analysis of Netravati-GurpurRivermouth Using Histogram Equalization and Adaptive Thresholding Techniques Raju Aedlaa*, Dwarakish G Sb, D Venkat Reddyc aDepartment of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka (NITK), Surathkal, Mangalore, India bDepartment of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka (NITK), Surathkal, Mangalore, India cDepartment of Civil Engineering, National Institute of Technology Karnataka (NITK), Surathkal, Mangalore, India Abstract The shoreline change extraction and change detection analysis is an important task that has application in different fields such as development of setback planning, hazard zoning, erosion-accretion studies, regional sediment budgets and conceptual or predictive modeling of coastal morphodynamics. Shoreline delineation is difficult, time consuming, and sometimes impossible for entire coastal system when using traditional ground survey techniques. Recent advances in remote sensing and geographical information system (GIS) techniques are overcoming the difficulties in extraction of shoreline position and detection of shoreline changes. In the present paper, an automatic shoreline detection method using histogram equalization and adaptive thresholding techniques is developed. The shoreline of Netravati-Gurpur rivermouth area along Mangalore coast, West Coast of India have been extracted from Indian Remote Sensing Satellite (IRS P6) LISS-III (2005, 2007 and 2010) and IRS R2 LISS-III (2013) satellite images using developed automatic shoreline detection method. The delineated shorelines have been analyzed using Digital Shoreline Analysis System (DSAS), a GIS Software tool for estimation of shoreline change rates through two statistical techniques such as, End Point Rate (EPR) and Linear Regression Rate (LRR). The Bengre spit, Northern sector of Netravati- Gurpur river mouth is under accretion an average of 2.95 m/yr (EPR) and 3.07 m/yr (LRR) and maximum accretion obtained is 8.51 m/yr (EPR) and 8.69 m/yr (LRR). Southern sector, the Ullal spit is under erosion an average of -0.56 m/yr (EPR) and -0.59 m/yr (LRR). ©© 20152015 Published The Authors. by Elsevier Published B.V. byThis Elsevier is an open B.V. access article under the CC BY-NC-ND license (Peerhttp://creativecommons.org/licenses/by-nc-nd/4.0/-review under responsibility of organizing committee). of ICWRCOE 2015. Peer-review under responsibility of organizing committee of ICWRCOE 2015 Keywords:Histogram equalization, thresholding, shoreline, remote sensing, digital shoreline analysis system * Corresponding author. E-mail address: [email protected] 2214-241X © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi: 10.1016/j.aqpro.2015.02.073 564 Raju Aedla et al. / Aquatic Procedia 4 ( 2015 ) 563 – 570 1. Introduction Coastal zones are one of the most complicated ecosystems with a large number of living and non-living resources. Coastal zones are exposed to a series of dynamic natural processes like coastal erosion, accretion, sediment transport, environmental pollution, and coastal development that usually causes changes in long and short term spans. Coastal zones are complicated ecosystems with a large number of living and non-living resources by Constanza et al. (1997). Coastal zones are major socio-economic environment in worldwide and these coastal changes impacts on loss of life and property, security of harbors, change of the coastal socio-economic environment, and decrease of coastal land resources. So, coastal zone monitoring is a significant task in national development and environmental protection, in which, extraction of shoreline is the fundamental study of necessity by Rasuly et al. (2010). Shoreline is considered as one of the most dynamic processes in coastal area by Bagli and Soile (2003); Mills et al. (2005) and it is the physical interface of land and water by Dolan et al. (1980). Shoreline is formed by a number of geological factors such as interaction, sediment deposition of rivers and oceans, various weather and sea conditions, as well as the frequent human social and economic activities by Boak and Turner (2005). The shoreline is one of the 27 features recognized by IGDC (International Geographic Data Committee) by Li et al. (2001). The location of the shoreline provides the data in respect to shoreline reorientation adjacent to structures by Komar (1998) and beach width and volume by Smith and Jackson (1992), and it is used to quantify historical rates of change by Dolan et al. (1991); Moore (2000). The extraction of shoreline is useful for several applications like coastline change detection and coastal zone management, and this task is difficult, time consuming, and sometimes impossible for entire coastal system when using traditional ground survey techniques by Cracknell (1999). Due to the preference and large effort involved in manual detection, quite a few automatic shoreline detection methods have been proposed. Advanced remote sensing and geographical information system (GIS) techniques are overcoming the difficulties in detecting shoreline position and shoreline change analysis. Several techniques have been developed to extract shoreline and change detection from satellite imagery such as, image enhancement, multi- temporal data classification and comparison of two independent land cover classifications, density slice using single or multiple bands, and multi-spectral classification, both supervised and unsupervised (like ISODATA, Principle Component Analysis (PCA), Tasseled Cap, NDWI) by Mas (1999); Frazier and Page (2000); Ryu et al. (2002); Braud and Feng (1998); Kuleli (2010); Kuleli et al. (2011); Zheng et al. (2011); Bouchahma and Yan (2012). Along with image classification methods, various thresholding based techniques have been proposed by Bayram et al. (2008); Jishuang and Chao (2002); White and Asmar (1999); Yamayo et al. (2006). In addition, image processing algorithms such as pre-segmentation, segmentation and post-segmentation have been proposed for automatic extraction of coastline from remotely sensed images by Liu and Jezek (2004); Mason and Davenport (1996); Di et al. (2003). In automatic shoreline extraction task, general-purpose edge detection and image segmentation techniques are not enough, because of lack of constant, sufficient intensity contrast between land and water regions and resulting complexity in separating shoreline edges from other object edges by Liu and Jezek (2004). Considerable contrast exists between land and water masses will generate continuous and clear shoreline. With this knowledge, the present study proposed a complete automatic shoreline extraction method from satellite imagery by using clipped histogram equalization based contrast enhancement and thresholding based techniques. Histogram Equalization (HE) is a well-known indirect contrast enhancement method, where histogram of the image is modified. Because of stretching the global distribution of the intensity, the information laid on the histogram or probability distribution function (PDF) of the image will be lost. To overcome the drawbacks of HE method, several HE-based techniques have been proposed. Based on the modification of input image histogram, the techniques are categorized into Bi-Histogram Equalization, Multi-Histogram Equalization and Clipping or Plateau HE methods by Raju et al. (2013a). Bi-HE methods by Kim (1997); Wang et al. (1999); Chen and Ramli (2003a); Chen and Ramli (2004); Sengee et al. (2010); Zuo et al. (2012) are preserving the brightness and enhance contrasts of the images up to certain limit and showing over-enhancement with annoying artefacts in the image. Multi-HE methods by Wongsritong et al. (1998); Chen and Ramli (2003b); Sim et al. (2007); Wadud et al. (2007); Ibrahim and Pik Kong (2007); Menotti et al. (2007); Kim and Chung (2008); Wadud et al. (2008); Sheet et al. (2010); Khan et al. (2012) providing well brightness preserving without introducing any undesirable artefacts, but sacrifices the contrast enhancement in the image. Clipping histogram equalization methods by Yang et al. (2003); Wang et al. (2006); Nicholas et al. (2009); Kim and Paik (2008); Ooi et al. (2009); Ooi and Isa (2010); Liang et al. (2012) are superior in controlling the Raju Aedla et al. / Aquatic Procedia 4 ( 2015 ) 563 – 570 565 enhancement rate, brightness preserving and avoiding over amplification of noise in the image. Contrast enhancement techniques emphasize the small or suppressed objects and object edges, resulting high positional accuracy of coastline through automatic detection. The present study was carried out with a view to develop an automatic shoreline extraction method using clipped histogram equalization based contrast enhancement for enhancing coastal pixels and thresholding techniques for segment water and land regions. DSAS software and multi-temporal IRS-P6 and IRS-R2 data has been used for the analysis of shoreline changes of Netravati-Gurpurrivermouth area, Mangalore Coast, West Coast of India. The present paper is organized in five sections. Section 1 gives brief introduction of coastal zone,
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