A Novel Algorithm for Coastline Fitting through a Case Study over the Bosphorus

Abstract Decrease of habitat, coastal erosion, and shoreline changes are recent issues for coastal management. In this study, an algorithm which extracts coastlines efficiently and automatically by processing low- or medium-resolution satellite images has been developed. The junction of sea and land is a common result yielded by the automatic coastline extraction method. In this study, CORONA (1963), IRS-1D (2000), and LANDSAT-7 (2001) satellite images for the same region in Istanbul, Turkey were used. A novel algorithm was developed for automatic coastline extraction. The algorithm is encoded in a C++environment. The results of automatic coastline extraction obtained from different images were compared to the results derived from manual digitizing. Random control points which are seen on every image were used. The average differences of selected points were calculated. Obtained results from selected points were rendered as 3 pixels on the CORONA and IRS-1D images and as 2 pixels on the LANDSAT-7 image. These show that the differences are similar, although different images were used. On the other hand, the results are acceptable compared to manual digitizing.

[1]  Frank Y. Shih,et al.  Automatic seeded region growing for color image segmentation , 2005, Image Vis. Comput..

[2]  Michel Jourlin,et al.  A new minimum variance region growing algorithm for image segmentation , 1997, Pattern Recognit. Lett..

[3]  Hans-Peter Meinzer,et al.  New methods for leak detection and contour correction in seeded region growing segmentation , 2004 .

[4]  A. Ardeshir Goshtasby,et al.  Interactive tools for image segmentation , 1999, Medical Imaging.

[5]  Inmaculada García,et al.  The load unbalancing problem for region growing image segmentation algorithms , 2003, J. Parallel Distributed Comput..

[6]  K. White,et al.  Monitoring changing position of coastlines using Thematic Mapper imagery, an example from the Nile Delta , 1999 .

[7]  D Z Seker,et al.  Investigation of coastal morphological changes due to river basin characteristics by means of remote sensing and GIS techniques. , 2003, Water science and technology : a journal of the International Association on Water Pollution Research.

[8]  Antonios Atsalakis,et al.  Gray-Level Reduction Using Local Spatial Features , 2000, Comput. Vis. Image Underst..

[9]  Ye Zhou,et al.  Segmentation of petrographic images by integrating edge detection and region growing , 2004, Comput. Geosci..

[10]  S. Nayak.  ROLE OF REMOTE SENSING TO INTEGRATED COASTAL ZONE MANAGEMENT , 2004 .

[11]  F. Lam,et al.  An iterative image segmentation algorithm utilizing spatial information , 1996, Proceedings of Digital Processing Applications (TENCON '96).

[12]  Lisheng Wang,et al.  Threshold selection by clustering gray levels of boundary , 2003, Pattern Recognit. Lett..

[13]  U. Acar,et al.  COAST LINE CHANGE DETECTION USING CORONA , SPOT and IRS 1 D IMAGES , 2004 .

[14]  Tianxu Zhang,et al.  Local entropy-based transition region extraction and thresholding , 2003, Pattern Recognit. Lett..

[15]  H. Liu,et al.  Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods , 2004 .

[16]  IMAGE PROCESSING AND GIS TOOLS FOR FEATURE AND CHANGE EXTRACTION , 2004 .

[17]  Jianping Fan,et al.  Automatic image segmentation by integrating color-edge extraction and seeded region growing , 2001, IEEE Trans. Image Process..

[18]  A. Yalciner,et al.  Determination and control of longshore sediment transport: A case study , 2007 .

[19]  Wang Chao,et al.  A MULTI-THRESHOLD BASED MORPHOLOGICAL APPROACH FOR EXTRACTING COASTAL LINE FEATURE IN REMOTE SENSED IMAGES , 2002 .

[20]  Saeid Belkasim,et al.  Phase-based optimal image thresholding , 2003, Digit. Signal Process..

[21]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[22]  Roland Wilson,et al.  Unsupervised image segmentation combining region and boundary estimation , 2000, Image Vis. Comput..

[23]  Zhihong Man,et al.  Edge based region growing: a new image segmentation method , 2004, VRCAI '04.

[24]  Xiaoli Yang,et al.  Watershed segmentation for binary images with different distance transforms , 2004, Proceedings. Second International Conference on Creating, Connecting and Collaborating through Computing.

[25]  Pong C. Yuen,et al.  A contour detection method: Initialization and contour model , 1999, Pattern Recognit. Lett..

[26]  Cheng-Chien Liu,et al.  Prediction of ocean colour: Monte Carlo simulation applied to a virtual ecosystem based on the Lagrangian Ensemble method , 2004 .

[27]  S. Dury,et al.  THE USE OF HYPERSPECTRAL DATA IN COASTAL ZONE VEGETATION MONITORING , 2004 .

[28]  D. Geneletti,et al.  Monitoring a Recent Delta Formation in a Tropical Coastal Wetland Using Remote Sensing and GIS. Case Study: Guapo River Delta, Laguna de Tacarigua, Venezuela , 2002 .

[29]  Nikolaos G. Bourbakis,et al.  A fuzzy region growing approach for segmentation of color images , 1997, Pattern Recognit..