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Communication Dans Un Congrès Année : 2018

Stacked Encoder-Decoders for Accurate Semantic Segmentation of Very High Resolution Satellite Datasets

Résumé

Semantic segmentation is a mainstream method in several remote sensing applications based on very-high-resolution data, achieving recently remarkable performance by the use of deep learning and more specifically, pixel-wise dense classification models. In this paper, we exploit the use of a relatively deep architecture based on repetitive downscale-upscale processes that had been previously employed for human pose estimation. By integrating such a model, we are aiming to capture low-level details, such as small objects, object boundaries and edges. Experimental results and quantitative evaluation has been performed on the publicly available ISPRS (WGIII/4) benchmark dataset indicating the potential of the proposed approach.
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Dates et versions

hal-01870857 , version 1 (09-09-2018)

Identifiants

  • HAL Id : hal-01870857 , version 1

Citer

Maria Papadomanolaki, Maria Vakalopoulou, Nikos Paragios, Konstantinos Karantzalos. Stacked Encoder-Decoders for Accurate Semantic Segmentation of Very High Resolution Satellite Datasets. IGARSS 2018 - 38th annual International Geoscience and Remote Sensing Symposium, Jul 2018, Valencia, Spain. pp.1-4. ⟨hal-01870857⟩
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