Deep learning for semantic segmentation of remote sensing images with rich spectral content

With the rapid development of Remote Sensing acquisition techniques, there is a need to scale and improve processing tools to cope with the observed increase of both data volume and richness. Among popular techniques in remote sensing, Deep Learning gains increasing interest but depends on the quality of the training data. Therefore, this paper presents recent Deep Learning approaches for fine or coarse land cover semantic segmentation estimation. Various 2D architectures are tested and a new 3D model is introduced in order to jointly process the spatial and spectral dimensions of the data. Such a set of networks enables the comparison of the different spectral fusion schemes. Besides, we also assess the use of a “noisy ground truth” (i.e. outdated and low spatial resolution labels) for training and testing the networks.

• Publication year

  2017

• Venue

  IEEE International Geoscience and Remote Sensing Symposium

• Publication date

  2017-07-23

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.1109/IGARSS.2017.8127520arXiv 1712.01600
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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