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.
Highlights
A user-interactive tool has been developed that enables the manual delineation of exemplative regions in the input image of specific classes in conjunction with a fully-connected conditional random field (CRF) to estimate the class for every pixel within the image
Training and evaluation datasets are created by selecting tiles from the image that contain a proportion of pixels that correspond to a given class that is greater than a given threshold
The retrained deep convolutional neural networks (DCNNs) is used to classify small spatially-distributed regions of pixels in a sample image, which is used in conjunction with the same CRF method used for label image creation to estimate a class for every pixel in the image
Summary
There is a growing need for fully-automated pixel-scale classification of large datasets of color digital photographic imagery to aid in the analysis and interpretation of natural landscapes and geomorphic processes. The task of classifying natural objects and textures in images of landforms is increasingly widespread in a wide variety of geomorphological research [1,2,3,4,5,6,7], providing the impetus for the development of completely automated methods to maximize speed and objectivity. There is a growing trend in studies of coastal and fluvial systems for using automated methods to extract information from time-series of imagery from fixed camera installations [10,11,12,13,14,15,16], UAVs [17,18,19], and other aerial platforms [20]. Numerous complementary or alternative uses of such imagery and elevation models for the purposes of geomorphic research include facies description and grain size
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