Artificial Neural Networks for Satellite Image Classification of Shoreline Extraction for Land and Water Classes of the North West Coast of Peninsular Malaysia

Abstract

Monitoring and measuring the shoreline of coastal zones helps establish the boundary of a country. Such an activity entails ground survey, topographic survey, aerial photo, or remote sensing techniques to extract the shoreline. For example, the remote sensing technique to determine shorelines involves the extraction of relevant data from satellite images. Specifically, the satellite image classification enables shorelines to be extracted from land and water classes with a high degree of precision. However, extracting information from satellite images is challenging as it relies on a strong understanding of image processing, machine learning, and data mining techniques. Thus, the researchers discuss the study of the pixel-based classification of machine learning techniques to classify land and water classes in terms of accuracy, training time, and testing time. The research findings showed that the Multilayer Perceptron Artificial Neural Network (MLP ANN) was the most effective technique, compared with other techniques, hence reinforcing its importance in classifying land and water classes.

Coastal Wetland Shoreline Change Monitoring: A Comparison of Shorelines from High-Resolution WorldView Satellite Imagery, Aerial Imagery, and Field Surveys

“…Techniques to reduce this effect are to increase transect frequency or creating curvilinear baselines that closely match the bends of the shoreline, however both options would require a greater time investment for data analyses. Other methodologies for evaluating shoreline changes over time, such as point-based techniques that evaluate distances between shoreline points [59,64], "fuzzy boundaries" techniques [65], Bayesian methods [15], or machine learning [66,67] may be appropriate for the gradual, indistinct boundaries common to wetland and estuarine shorelines. Calkoen et al [68] evaluated machine learning techniques against ordinary least squares regression techniques (the transect-based approach explored here) to predict future shoreline change, but research that evaluates different shoreline extraction methods and their impact on statistical calculation of shoreline change rates for estuarine shorelines is a topic that warrants greater attention.…”

Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network

“…ANNs have gained popularity in the last decades with successful applications in many fields such as sediment transport [16] , satellite image classification [26], evapotranspiration [27], coastal erosion [28] etc. They are a very powerful computational technique for complex non-linear relationships.…”

Hybridization of SLIC and Extra Tree for Object Based Image Analysis in Extracting Shoreline from Medium Resolution Satellite Images

“…For this study, only one set of training and testing samples was created in the form of polygons, comprising 260 and 65 polygons for land and water classes, respectively. To ensure separability of the training and testing set, JeffriesMatusita distance and Transformed Divergence were used [9], yielding a separability index of 1.97, which was close to 2.0 that indicated perfect separability.…”

“…For the extraction process, the researchers performed OBIA using 15 ML classifiers (11 single classifiers and 4 ensemble classifiers) to classify land-water classes. The proposed OBIA techniques are in fact the alternative approaches to the pixel-based classification methods, which have been studied by [9].…”