Landscape classification with deep neural networks.

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Authors

Daniel Buscombe 

Abstract

The application of deep learning, specifically deep convolutional neural networks (DCNNs), to the classification of remotely sensed imagery of natural landscapes has the potential to greatly assist in the analysis and interpretation of geomorphic processes. However, the general usefulness of deep learning applied to conventional photographic imagery at a landscape scale is, at yet, largely unproven. If DCNN-based image classification is to gain wider application and acceptance within the geoscience community, demonstrable successes need to be coupled with accessible tools to retrain deep neural networks to discriminate landforms and land uses in landscape imagery. Here, we present an efficient approach to train/apply DCNNs with/on sets of photographic images, using a powerful graphical method, called a conditional random field (CRF), to generate DCNN training and testing data using minimal manual supervision. We apply the method to several sets of images of natural landscapes, acquired from satellites, aircraft, unmanned aerial vehicles, and fixed camera installations. We synthesize our findings to examine the general effectiveness of transfer learning to landscape scale image classification. Finally, we show how DCNN predictions on small regions of images might be used in conjunction with a CRF for highly accurate pixel-level classification of images.

DOI

https://doi.org/10.31223/osf.io/5mx3c

Subjects

Computer and Systems Architecture, Computer Engineering, Earth Sciences, Engineering, Environmental Monitoring, Environmental Sciences, Geology, Geomorphology, Other Statistics and Probability, Physical Sciences and Mathematics, Statistics and Probability

Keywords

remote sensing, machine learning, Deep learning, land use, image classification, land cover, Image Segmentation, aerial imagery, landforms, UAS

Dates

Published: 2018-06-18 17:35

Last Updated: 2018-06-27 21:03

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License

GNU Lesser General Public License (LGPL) 2.1

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