Going Deep into Remote Sensing Spatial Feature Learning

  • Keiller Nogueira UFMG
  • William Robson Schwartz UFMG
  • Jefersson Alex dos Santos UFMG

Resumo


A lot of information may be extracted from the Earth’s surface through aerial images. This information may assist in myriad applications, such as urban planning, crop and forest management, disaster relief, etc. However, the process of distilling this information is strongly based on efficiently encoding the spatial features, a challenging task. Facing this, Deep Learning is able to learn specific data-driven features. This PhD thesis1 introduces deep learning into the remote sensing domain. Specifically, we tackled two main tasks, scene and pixel classification, using Deep Learning to encode spatial features over high-resolution remote sensing images. First, we proposed an architecture and analyze different strategies to exploit Convolutional Networks for image classification. Second, we introduced a network and proposed a new strategy to better exploit multi-context information in order to improve pixelwise classification. Finally, we proposed a new network based on morphological operations towards better learning of some relevant visual features.
Palavras-chave: deep learning, machine learning, remote sensing, image classification, image segmentation.

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Publicado
07/11/2020
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NOGUEIRA, Keiller; SCHWARTZ, William Robson; DOS SANTOS, Jefersson Alex. Going Deep into Remote Sensing Spatial Feature Learning. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 33. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 98-104. DOI: https://doi.org/10.5753/sibgrapi.est.2020.12990.