Automated Coastline Extraction Using Edge Detection Algorithms

TL;DR

This study evaluates and compares four edge detection algorithms for automatic coastline extraction from satellite images, highlighting their effectiveness and the impact of preprocessing techniques like histogram equalization and Gaussian blur.

Contribution

It provides a comparative analysis of Canny, Sobel, Scharr, and Prewitt algorithms for coastline detection, including the effects of image preprocessing methods.

Findings

Canny achieved the highest SSIM of 0.8 among the algorithms.

Preprocessing with histogram equalization and Gaussian blur improved detection accuracy.

Canny struggled with noisy edges, such as those caused by development activities.

Abstract

We analyse the effectiveness of edge detection algorithms for the purpose of automatically extracting coastlines from satellite images. Four algorithms - Canny, Sobel, Scharr and Prewitt are compared visually and using metrics. With an average SSIM of 0.8, Canny detected edges that were closest to the reference edges. However, the algorithm had difficulty distinguishing noisy edges, e.g. due to development, from coastline edges. In addition, histogram equalization and Gaussian blur were shown to improve the effectiveness of the edge detection algorithms by up to 1.5 and 1.6 times respectively.