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Remote Sens. 2018, 10(7), 1119; https://doi.org/10.3390/rs10071119

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

1
C-CORE and Department of Electrical Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
2
CRC-Laboratory in Advanced Geomatics Image Processing, Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
*
Author to whom correspondence should be addressed.
Received: 24 May 2018 / Revised: 30 June 2018 / Accepted: 12 July 2018 / Published: 14 July 2018
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Abstract

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands. View Full-Text
Keywords: deep learning; Convolutional Neural Network; machine learning; multispectral images; land cover classification; wetland; RapidEye; full-training; fine-tuning deep learning; Convolutional Neural Network; machine learning; multispectral images; land cover classification; wetland; RapidEye; full-training; fine-tuning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Mahdianpari, M.; Salehi, B.; Rezaee, M.; Mohammadimanesh, F.; Zhang, Y. Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery. Remote Sens. 2018, 10, 1119.

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