Abstract
Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use of machine learning algorithms to classify complex environments, specifically in remote sensing. In this paper, we explore the potential and possible limitations to be overcome regarding the use of ensemble deep learning techniques for complex wetland classification and discusses the potential and limitation of various solo convolutional neural networks (CNNs), including DenseNet, GoogLeNet, ShuffleNet, MobileNet, Xception, Inception-ResNet, ResNet18, and ResNet101 in three different study areas located in Newfoundland and Labrador, Canada (i.e., Avalon, Gros Morne, and Grand Falls). Moreover, to improve the classification accuracies of wetland classes of bog, fen, marsh, swamp, and shallow water, the results of the three best CNNs in each study area is fused using three supervised classifiers of random forest (RF), bagged tree (BTree), Bayesian optimized tree (BOT), and one unsupervised majority voting classifier. The results suggest that the ensemble models, in particular BTree, have a valuable role to play in the classification of wetland classes of bog, fen, marsh, swamp, and shallow water. The ensemble CNNs show an improvement of 9.63–19.04% in terms of mean producer’s accuracy compared to the solo CNNs, to recognize wetland classes in three different study areas. This research indicates a promising potential for integrating ensemble-based learning and deep learning for operational large area land cover, particularly complex wetland type classification.
Highlights
We examine the ability of a proposed ensemble convolutional neural networks (CNNs) model with two different classification strategies: (1) employing majority voting in the last layer; (2) applying a machine learning classifier, including random forest (RF), bagged tree (BTree), and Bayesian optimized tree (BOT)
The proposed framework can be summarized in four steps: (1) evaluate the performance of each solo CNN model for wetland classification using multi-spectral RapidEye satellite data, (2) select the best three CNN models based on accuracy assessments indices, (3) apply ensemble modeling using two different strategies of majority voting and employing supervised machine learning models (i.e., RF, BTree, and BOT), (4) Evaluate of results of solo versus ensemble CNN models for wetland classification
This research phase consisted of results evaluation designed to assess if solo CNNs can detect complex wetland classes to an acceptable accuracy
Summary
Wetlands cover 3% to 8% of the Earth’s land surface and are amongst the most valuable ecosystems across the world [1]. Wetlands make invaluable contributions to the maintenance and quality of life for nature and humanity. Food security, water storage, as well as flood and shoreline protection are only some of the services provided by wetlands [4,5]. They provide critical habitat that supports plant and animal biodiversity [6,7].
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