A Synergic Use of Sentinel-1 and Sentinel-2 Imagery for Complex Wetland Classification Using Generative Adversarial Network (GAN) Scheme

Ali Jamali,Masoud Mahdianpari,Fariba Mohammadimanesh,Bahram Salehi,Brian Brisco

doi:10.3390/w13243601

Abstract

Due to anthropogenic activities and climate change, many natural ecosystems, especially wetlands, are lost or changing at a rapid pace. For the last decade, there has been increasing attention towards developing new tools and methods for the mapping and classification of wetlands using remote sensing. At the same time, advances in artificial intelligence and machine learning, particularly deep learning models, have provided opportunities to advance wetland classification methods. However, the developed deep and very deep algorithms require a higher number of training samples, which is costly, logistically demanding, and time-consuming. As such, in this study, we propose a Deep Convolutional Neural Network (DCNN) that uses a modified architecture of the well-known DCNN of the AlexNet and a Generative Adversarial Network (GAN) for the generation and classification of Sentinel-1 and Sentinel-2 data. Applying to an area of approximately 370 sq. km in the Avalon Peninsula, Newfoundland, the proposed model with an average accuracy of 92.30% resulted in F-1 scores of 0.82, 0.85, 0.87, 0.89, and 0.95 for the recognition of swamp, fen, marsh, bog, and shallow water, respectively. Moreover, the proposed DCNN model improved the F-1 score of bog, marsh, fen, and swamp wetland classes by 4%, 8%, 11%, and 26%, respectively, compared to the original CNN network of AlexNet. These results reveal that the proposed model is highly capable of the generation and classification of Sentinel-1 and Sentinel-2 wetland samples and can be used for large-extent classification problems.

Highlights

Wetlands have been identified as one of the most valuable ecosystems on Earth for both fauna and flora in recent decades
The developed Deep Convolutional Neural Network (DCNN) model achieved a high level of accuracy in terms of F-1 scores with values of 0.82, 0.85, 0.87, 0.89, and 0.95 for the recognition of swamp, fen, marsh, bog, and shallow water, respectively, using extracted features of Sentinel-1 and Sentinel-2 images
The highest F-1, recall, and precision values were obtained for the recognition of the bog and shallow water compared to the other wetland classes of the fen, marsh, and swamp

Summary

Introduction

Wetlands have been identified as one of the most valuable ecosystems on Earth for both fauna and flora in recent decades. Coastline protection, carbon and other nutrient processing, food security, and the support of huge biodiversity of plants and animals are some of the significant aspects of wetlands, depending on the wetland type [1,2]. Despite their necessity, wetlands are declining at a rate greater than any other environment, owing primarily to global climate change, as well as anthropogenic activities (e.g., urbanization and industrialization) [2]. The necessity for complete wetland inventories and subsequent monitoring capabilities to determine status and trends is essential, as it provides the foundation for directing effective evaluation, monitoring, and management of wetlands [3]

Methods

Results

Discussion

Conclusion