Rocky Shoreline Extraction Using a Deep Learning Model and Object-Based Image Analysis

Abstract

Abstract. In the context of the increasing anthropogenic influence on the coastal areas that are subject to high climate variability, the main challenge is to understand its current dynamics and to predict its future evolution. Therefore, monitoring of the shoreline kinematics is a key factor for the coastal erosion assessment and an essential feature for the sustainable management of these naturally vulnerable areas.This work focuses on the detection and extraction of the shoreline, basing on a specific remote sensing methodology using Very High Resolution (VHR) optical images. Indeed, an integrated approach based on a Deep Learning model, which is the Convolutional Neural Network (CNN) and Object Based Image Analysis (OBIA) has been developed. This study aims to evaluate the methodological contribution of this integrated approach for the (semi)-automatic extraction of the rocky shoreline, for which the botanical indicator has been chosen. Therefore the upper limit of black marine lichen has been detected and extracted as the target shoreline. It is the first indication of a (semi)-automatic detection of such a complex type of shoreline.The classification results derived from the combined CNN model and OBIA methods had achieved a high overall accuracy of 0.94. The extracted shoreline have been compared to a shoreline of reference derived from a traditional method that is a manual digitizing. The distances between the two shorelines has been calculated in order to assess the accuracy of the extraction method. This comparison revealed that 76% of the extracted shoreline lies within 1 m, and 35% lies within 0.5 m of reference one. Therefore, the CNN model integrated to OBIA was successfully shown to be a good method for shoreline extraction and could offer an immediate insight regarding rocky shoreline position, providing an alternative to its monitoring.

Multiscale Object-Based Classification and Feature Extraction along Arctic Coasts

“…The scale parameter is used to control average image object size [41,42] where a higher value results in bigger objects and a smaller value results in smaller objects. The scale parameter has been considered the primary factor for segmentation in object-based research [43,44] and the determination of the scale parameter depends on factors such as the sensor type, resolution, the purpose of the segmentation, and objects of interest. As a result, we incremented our scale parameter by 100 between 100 and 800 in our threshold-based and supervised classifications to better understand the influence of object size on accuracy.…”

“…Object-based and deep learning applications have been applied to infrastructure detection in the Arctic [32], ice-wedge polygon mapping [33][34][35], and recently in detecting RTS [29]. Broadly, deep learning neural networks have been successfully demonstrated in image classification, segmentation, and object detection, leading to substantial application in remote sensing [36][37][38][39][40][41], coastal erosion [42][43][44][45], and geomorphology [46][47][48].…”

A radiometric contouring approach to map the shoreline

“…Pixel-based approaches can be further categorized into image thresholding, water indices 10 , raster-based approaches 11 , and vector-based 12 . We introduce a novel methodology based on raster contouring, wherein the shoreline is delineated by treating pixels as concurrent points of radiometric remote measurements.…”

A Method for Optimal Estimation of Shoreline in Cliff Zones Based on Point Cloud Segmentation and Centroid Calculation

“…Therefore, many effective and reliable extraction algorithms suitable for specific shoreline types have been proposed through analyses of data characteristics of each type. Bengoufa et al [20] proposed the use of deep learning and object-oriented analysis for bedrock shoreline extraction. Weifu et al [21] established different interpretation marks according to different shoreline types using remote sensing data and designed different shoreline extraction methods.…”