Seawall detection in Florida coastal area from high-resolution imagery using machine learning and OBIA

Abstract

A methodology and framework were presented to detect the seawalls accurately and efficiently in low coastal areas and were then evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected by the Florida Department of Transportation were processed using eCognition Developer software for multiresolution segmentation and classification of objects. Two classification approaches, pixel-based image analysis and the object-based image analysis method, were applied for image classification. However, pixel-based classification was discarded for having less accuracy in output. Three techniques within object-based classification-machine learning (ML) technique, knowledge-based (KB) technique, and ML followed by KB technique were used to compare the most efficient method of classification. While performing the ML technique, three algorithms, such as random forest, support vector machine, and decision tree, were applied to test the best algorithm. Of all the approaches used, the combination of ML and a KB method was able to map the seawall effectively with an overall accuracy of 94%.