Automated Coastline Extraction Using Edge Detection Algorithms
This work addresses coastline extraction for remote sensing applications, but it is incremental as it applies existing methods to a specific domain without major innovations.
The study tackled the problem of automatically extracting coastlines from satellite images by comparing edge detection algorithms, finding that Canny performed best with an average SSIM of 0.8, and pre-processing techniques like histogram equalization and Gaussian blur improved effectiveness by up to 1.6 times.
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.