16
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

      Preprint
      , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          New remote sensing sensors acquire now high spatial and spectral Satellite Image Time Series (SITS) of the world. These series of images are a key component of any classification framework to obtain up-to-date and accurate land cover maps of the Earth's soils. More specifically, the combination of the temporal, spectral and spatial resolutions of new SITS enables the monitoring of vegetation dynamics. Although some traditional classification algorithms, such as Random Forest (RF), have been successfully applied for SITS classification, these algorithms do not fully take advantage of the temporal domain. Conversely, deep-learning based methods have been successfully used to make the most of sequential data such as text and audio data. For the first time, this paper explores the use of Convolutional Neural Networks (CNNs) with convolutions applied in the temporal dimension for SITS classification. The goal is to quantitatively and qualitatively evaluate the contribution of temporal CNNs for SITS classification. More precisely, this paper proposes a set of experiments performed on a million Formosat-2 time series. The experimental results show that temporal CNNs are 2 to 3 % more accurate than RF. The experiments also highlight some counter-intuitive results on pooling layers: contrary to image classification, their use decreases accuracy. Moreover, we provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as the batch size. Finally, the visual quality of the land cover maps produced by the temporal CNN is assessed.

          Related collections

          Most cited references41

          • Record: found
          • Abstract: not found
          • Article: not found

          Random forest in remote sensing: A review of applications and future directions

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The importance of land-cover change in simulating future climates.

              Adding the effects of changes in land cover to the A2 and B1 transient climate simulations described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change leads to significantly different regional climates in 2100 as compared with climates resulting from atmospheric SRES forcings alone. Agricultural expansion in the A2 scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans. These and other influences on the Hadley and monsoon circulations affect extratropical climates. Agricultural expansion in the mid-latitudes produces cooling and decreases in the mean daily temperature range over many areas. The A2 scenario results in more significant change, often of opposite sign, than does the B1 scenario.
                Bookmark

                Author and article information

                Journal
                25 November 2018
                Article
                1811.10166
                7825f98a-7e8a-416f-95c7-87f7338b9f32

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                cs.CV

                Computer vision & Pattern recognition
                Computer vision & Pattern recognition

                Comments

                Comment on this article