Next Article in Journal
Quantifying Hydrothermal Alteration: A Review of Methods
Previous Article in Journal
PHOTOSED—PHOTOgrammetric Sediment Erosion Detection
Open AccessArticle

Landscape Classification with Deep Neural Networks

1
School of Earth Sciences & Environmental Sustainability, Northern Arizona University, Flagstaff, AZ 86011, USA
2
Pacific Coastal and Marine Science Center, U.S. Geological Survey, Santa Cruz, CA 95060, USA
*
Author to whom correspondence should be addressed.
Geosciences 2018, 8(7), 244; https://doi.org/10.3390/geosciences8070244
Received: 18 June 2018 / Revised: 27 June 2018 / Accepted: 28 June 2018 / Published: 2 July 2018
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. View Full-Text
Keywords: image classification; image segmentation; land use; land cover; landforms; deep learning; machine learning; unmanned aerial systems; aerial imagery; remote sensing image classification; image segmentation; land use; land cover; landforms; deep learning; machine learning; unmanned aerial systems; aerial imagery; remote sensing
Show Figures

Figure 1

MDPI and ACS Style

Buscombe, D.; Ritchie, A.C. Landscape Classification with Deep Neural Networks. Geosciences 2018, 8, 244.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop