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首页> 外文期刊>Network Daily News >Study Results from Beijing Normal University Provide New Insight into Networks (Understanding the Role of Receptive Field of ConvolutionalNeural Network for Cloud Detection In Landsat 8 Oli Imagery)
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Study Results from Beijing Normal University Provide New Insight into Networks (Understanding the Role of Receptive Field of ConvolutionalNeural Network for Cloud Detection In Landsat 8 Oli Imagery)

机译:北京师范大学的研究结果提供新的见解网络(理解的接受域的作用ConvolutionalNeural网络云检测在地球资源观测卫星8奥利图像)

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摘要

By a News Reporter-Staff News Editor at Network Daily News – Research findings on Networks are discussed in a new report. According to news reporting originating in Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Deep semantic segmentation networks perform better in cloud detection of satellite imagery than traditional methods due to their ability to extract high-level features over a large receptive field. However, a large receptive field often leads to loss of spatial details and blurring of boundaries.”
机译:由一个新闻记者在网络新闻编辑每日新闻,研究发现在网络上在一份新的报告中讨论。来自北京的报道,人民中华民国,NewsRx记者,研究说,“深度语义分割在云检测网络表现得更好由于卫星图像比传统的方法高层特征的提取能力庞大的领域。接受域通常会导致损失的空间细节和模糊边界。”

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