• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >An End-to-End Oil-Spill Monitoring Method for Multisensory Satellite Images Based on Deep Semantic Segmentation
【2h】

An End-to-End Oil-Spill Monitoring Method for Multisensory Satellite Images Based on Deep Semantic Segmentation

机译：基于深度语义分割的多传感器卫星图像端到端漏油监测方法

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In remote-sensing images, a detected oil-spill area is usually affected by spot noise and uneven intensity, which leads to poor segmentation of the oil-spill area. This paper introduced a deep semantic segmentation method that combined a deep-convolution neural network with the fully connected conditional random field to form an end-to-end connection. On the basis of Resnet, it first roughly segmented a multisource remote-sensing image as input by the deep convolutional neural network. Then, we used the Gaussian pairwise method and mean-field approximation. The conditional random field was established as the output of the recurrent neural network. The oil-spill area on the sea surface was monitored by the multisource remote-sensing image and was estimated by optical image. We experimentally compared the proposed method with other models on the dataset established by the multisensory satellite image. Results showed that the method improved classification accuracy and captured fine details of the oil-spill area. The mean intersection over the union was 82.1%, and the monitoring effect was obviously improved.
机译：在遥感影像中，检测到的溢油区域通常会受到斑点噪声和强度不均匀的影响，这会导致溢油区域的分割不佳。本文介绍了一种深度语义分割方法，该方法将深度卷积神经网络与完全连接的条件随机字段组合在一起以形成端对端连接。在Resnet的基础上，它首先将深度卷积神经网络输入的多源遥感图像粗略分割。然后，我们使用了高斯成对方法和均值场近似。建立条件随机场作为递归神经网络的输出。通过多源遥感图像监测海面的溢油面积，并通过光学图像进行估计。我们在多感官卫星图像建立的数据集上实验地比较了所提出的方法和其他模型。结果表明，该方法提高了分类准确性，并捕获了漏油区域的精细细节。工会平均交集率为82.1％，监测效果明显提高。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号