Coastline Extraction from Aerial Images Based on Edge Detection

2002

While executing tasks such as sea surveilling, maritime searching and rescue, sea pollution monitoring utilizing remote sensed images, the coastal line feature should be determined at first. Thresholding methods is a type of simple but valid methods for image segmentation, likewise, they can be used to detect coastal line feature in remote sensed images. However, while conventional thresholding methods used

The objective of this paper is to introduce a new method for coastlines delineation from high-resolution satellite images automatically. The proposed approach integrates the color histogram analysis for estimating the number of clusters, fuzzy c-partition, and genetic algorithms for optimizing fuzzy c-partition matrix. The imagery is classified into homogenous areas using the proposed fuzzy logic segmentation approach. Morphological filtering is then used to remove noises from the segmented image. The coastlines are finally extracted from the filtered image by a delineation algorithm. The developed approach is evaluated based on a 1 m resolution IKONOS pansharpened imagery of Konarak region in Chabahar, Iran. It is demonstrated that 95% of the extracted lines remain within the three-pixels buffer areas around the coastlines. This presents the effectiveness of the proposed algorithm. Keywords: Satellite images, Feature extraction, Colour histogram, Fuzzy segmentation, Genetic algorithm

IEEE Access

This study highlights the coastline position changes of Qingdao coastal area from 2000 to 2019, using GIS and remote sensing technologies through Digital Shoreline Analysis System and LANDSAT images. Understanding the coastline movement by suitable method is an important challenge for this extremely dynamic coast. The shoreline changes were statistically measured using three techniques, namely; Linear Regression Rate, End Point Rate and Net Shoreline Movement. For the automatic coastline extraction, different methods were applied, but among them most suitable techniques is the canny edge algorithm technique, which gives the accurate result. The result show maximum accretion reached was 266.07m/yr, 2391.85m,124.47m/yr for End point rate, net shoreline movement and linear regression rate, respectively. While, the maximum erosion was-142.55m/yr,-1234.59m,-63.22m/yr for End point rate, net shoreline movement and linear regression rate, respectively. This paper hence presents the monitoring processes of coast and analyzing the coastline change by the use of geospatial techniques that would be helpful for the coastal planning and management of the Qingdao coast. The applicability of the proposed model is tested with other generic edge detection algorithms that include; Sobel, Prewitt, and Robert edge detection techniques and it was concluded that our model outperforming in accurately detecting the coastline. INDEX TERMS Coastline detection, canny edge detection, digital shoreline analysis system (DSAS), GIS, remote sensing, and change detection.

Applied Sciences

The accurate extraction of a coastline is necessary for various studies of coastal processes, as well as for the management and protection of coastal areas. Very high-resolution satellite imagery has great potential for coastline extraction; however, noises in spectral data can cause significant errors. Here, we present a newly developed Coastal Extraction Tool (CET) that overcomes such errors and allows accurate and time-efficient automated coastline extraction based on a combination of WorldView-2 (WV-2) multispectral imagery and stereo-pair-derived digital surface model (DSM). Coastline extraction is performed and tested on the Iž-Rava island group, situated within the Northern Dalmatian archipelago (Croatia). Extracted coastlines were compared to (a) coastlines extracted from state topographic map (1:25,000), and (b) coastline extracted by another available tool. The accuracy of the extracted coastline was validated with centimeter accuracy reference data acquired using a UAV sy...

Remote Sensing in Earth Systems Sciences, 2020

A shoreline is a highly dynamic part of the earth's surface. Advanced remote sensing (RS) and geographic information system (GIS) techniques are being used for detection of shoreline position and change analysis. In this paper, a new methodology for automatic shoreline extraction is demonstrated and analyzed using Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) images. The methodology involves several stages consisting of preprocessing of satellite images, band selection, coastal water index (CWI) preparation, normalization of binary images, Otsu thresholding technique (named after Nobuyuki Otsu) for the land and water separation, image noise correction with morphological filter (image morphology), seawater separation from waterbody, vectorization of classified binary image, polyline conversion from polygon vector, shoreline selection, and generalization of the final shoreline. The positional accuracy of the final shoreline is evaluated with expert captured shoreline. It was observed that the average positional difference between computer generated shoreline and expert digitized shoreline was less than a pixel resolution. The proposed methodology is very helpful in any coastal application where the shoreline is used as a parameter. It also reduces the time of human intervention.

Proceedings of the 4th GEOBIA, 2012

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Journal of Coastal Research, 1992

_ SHOSHANY, M., DEGANI, A., 1992. Shoreline detection by digital image processing of aerial photography. Journal of Coastal Research, 8(1), 29-34. Fort Lauderdale (Florida). ISSN 0749-0208.

International Journal of Remote Sensing, 2016

Coastline extraction from synthetic aperture radar (SAR) data is difficult because of the presence of speckle noise and strong signal returns from the wind-roughened and wave-modulated sea surface. High resolution and weather change independent of SAR data lead to better monitoring of coastal sea. Therefore, SAR coastline extraction has taken up much interest. The active contour method is an efficient algorithm for the edge detection task; however, applying this method to high-resolution images is timeconsuming. The current article presents an efficient approach to extracting coastlines from high-resolution SAR images. First, fuzzy clustering with spatial constraints is applied to the input SAR image. This clustering method is robust for noise and shows good performance with noisy images. Next, binarization is carried out using Otsu's method on the fuzzification results. Third, morphological filters are used on the binary image to eliminate spurious segments after binarization. To extract the coastline, an active contour level set method is used on the initial contours and is applied to the input SAR image to refine the segmentation. Because the proposed approach is based on an active contour model, it does not require preprocessing for SAR speckle reduction. Another advantage of the proposed method is the ability to extract the coastline at full resolution of the input SAR image without degrading the resolution. The proposed approach does not require manual initialization for the level set method and the proposed initialization speeds up the level set evolution. Experimental results on low-and high-resolution SAR images showed good performance for coastline extraction. A criterion based on neighbourhood pixels for the coastline is proposed for the quantitative expression of the accuracy of the method.

In the last 50 years, the inhabitants of the 19 municipalities along the Abruzzo coast have doubled and a stronger impact of the activities connected with the tourism has been experienced. The area, naturally exposed to the effects of changes of the sea level, has been characterized by a dramatic increase of erosion, due to the reduction of the solid transport from rivers to sea, as a consequence of extensive works carried out on the watersheds, to mitigate extreme rainfall and consequent flooding. The availability of data acquired by different sensors on the last few decades might be useful to assess the overall accretion/erosion trend of coastline, whereas combination of different observations taken in a restricted timeframe, may provide interesting inputs for detailed studies. In the present study, a methodology is proposed for the coastline identification from WorldView-2 images, available in 8 spectral bands, with 0.5 m and 1.8 m of spatial resolution for the panchromatic and the multispectral channels respectively. In particular, a pixel based multispectral classification is used to identify various types of land cover. The 8 bands allow to get good results, both in the classification process and with NDVI, NDWI, SAM, FM algorithms, for the identification of various land cover and in particular to separate dry from wet sand. Interesting results were obtained testing an algorithm to evaluate the relative depth of the water using the "coastal blue" band. Interesting results provided the comparison of the estimated coastline with such methodology and a topographic map of same area. This comparison highlights the changes in the study area. There are many possible applications of the proposed techniques, such as map updates and coastal change monitoring.

2004

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