Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images
نویسندگان
چکیده
Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement remote sensing technologies, such as satellites, drones, UAVs, airborne vehicles, large number high-resolution are readily available. However, these complex due to increased spatial data disruption caused by different factors involved acquisition process. Due challenges, an land-cover model is difficult design develop. In this paper, we develop hybrid deep learning that combines benefits two models, i.e., DenseNet U-Net. This carried out obtain pixel-wise classification cover. The contraction path U-Net replaced with extract features multiple scales, while long-range connections concatenate encoder decoder paths used preserve low-level features. We evaluate proposed network on challenging, publicly available benchmark dataset. From experimental results, demonstrate exhibits state-of-the-art performance beats other existing models considerable margin.
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ژورنال
عنوان ژورنال: Information
سال: 2021
ISSN: ['2078-2489']
DOI: https://doi.org/10.3390/info12060230