Automatic coastline extraction from aerial images using edge detection and optimization procedures

Summarization: Nowadays, coastline detection and formation monitoring has become of high importance due to the increasing effects of climate change, global warming and rapid growth of human population on the morphological characteristics of coasts. Coastal areas play a significant role on the economy of an entire region especially in major tourist destinations, where tourist infrastructure has been developed in the last decades. This thesis addresses the problem of coastline extraction and morphological change monitoring/tracking in the course of decades. The proposed methodology presents an automatic coastline extraction algorithm, which utilizes grayscale aerial images. Our method is summarized in a four-step algorithm that enables robust extraction of the coastline. Specifically, the first step of our approach is a noise distortion reduction stage with the goal of ameliorating its impact on the input data. The subsequent step involves the application of a local threshold in the input image to segment it into a binary class image, land and sea. The result is then passed to the third stage of our approach, which applies morphological operators on the binary image, with the aim of removing small objects and preserving only the objects of interest. Finally in the last stage of our method, we perform edge detection and active contours fitting, in order to extract and model the coastline. The aforementioned algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology. The algorithm is also applicable to one band satellite imagery due to the fact they have same characteristics with grayscale aerial images