Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study

Grayson R Morgan,Steven R Schill,Cuizhen Wang,Zhenlong Li,Daniel R Morgan

doi:10.3390/ijgi11020100

Abstract

Deep learning techniques are increasingly being recognized as effective image classifiers. Aside from their successful performance in past studies, the accuracies have varied in complex environments, in comparison with the popularly of applied machine learning classifiers. This study seeks to explore the feasibility of using a U-Net deep learning architecture to classify bi-temporal, high-resolution, county-scale aerial images to determine the spatial extent and changes of land cover classes that directly or indirectly impact tidal marsh. The image set used in the analysis is a collection of a 1-m resolution collection of National Agriculture Imagery Program (NAIP) tiles from 2009 and 2019, covering Beaufort County, South Carolina. The U-Net CNN classification results were compared with two machine learning classifiers, the random trees (RT) and support vector machine (SVM). The results revealed a significant accuracy advantage in using the U-Net classifier (92.4%), as opposed to the SVM (81.6%) and RT (75.7%) classifiers, for overall accuracy. From the perspective of a GIS analyst or coastal manager, the U-Net classifier is now an easily accessible and powerful tool for mapping large areas. Change detection analysis indicated little areal change on marsh extent, though increased land development throughout the county has the potential to negatively impact the health of the marshes. Future work should explore applying the constructed U-Net classifier to coastal environments in large geographic areas, while also implementing other data sources (e.g., LIDAR and multispectral data) to enhance classification accuracy.

Highlights

Machine learning (ML) algorithms have become commonplace in remote sensing data analysis [1,2,3,4,5,6,7,8]
The successful use of ML for a variety of GIS and remote sensing applications has led to the implementation of these methods, often based on support vector machine (SVM) and random forest (RF) statistical methods, into GIScience software packages that can be used by non-technical investigators
The mathematical and coding application of these classifiers were left to how the ESRI development team designed them, in ArcGIS Pro 2.8.1, in order to best represent what is available to the coastal managers and scientists with access to these common tools

Summary

Introduction

Machine learning (ML) algorithms have become commonplace in remote sensing data analysis [1,2,3,4,5,6,7,8]. The successful use of ML for a variety of GIS and remote sensing applications has led to the implementation of these methods, often based on support vector machine (SVM) and random forest (RF) statistical methods, into GIScience software packages that can be used by non-technical investigators. Numerous studies have supported the use of machine learning over traditional, i.e., statistically-based classifiers, such as maximum likelihood methods, with SVM often performing the best [11,12,13,14]. ML classifiers have been established across the professional community as reliable tools for mapping, without requirement of extensive machine learning and programming experiences. An advanced subsection of ML, DL is able to perform artificial intelligence functions with extensive training resources

Methods

Results

Discussion

Conclusion