...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Scale Sensitive Neural Network for Road Segmentation in High-Resolution Remote Sensing Images
【24h】

Scale Sensitive Neural Network for Road Segmentation in High-Resolution Remote Sensing Images

机译：高分辨率遥感图像中道路分割规模敏感神经网络

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Road segmentation in remote sensing images has been widely used in many fields. Semantic segmentation, based on deep learning, has become a hot topic for road segmentation. With the deepening of convolutional neural network (CNN) structures, features in the convolution layer that has more semantic information become more important for road segmentation. However, the spatial resolution of the convolutional layer reduced as the CNN network deepens, which causes the extracted roads to lose some important location information. To solve this problem, this letter proposes a novel end-to-end road segmentation method to effectively utilize the different levels of convolutional layers to enhance the model's ability to precisely perceive road edges and shapes. The model includes an encoder and a decoder. The encoder encodes the image to obtain the features of different levels and scales. The decoder consists of two modules: scale fusion module and scale sensitive module. In the scale fusion module, features in pooling layers of different scales are fused to obtain a fusion feature. In a scale sensitive module, a weight tensor at the end of the network is learned to evaluate the importance of fusion features. This road segmentation network has been experimentally verified using public data sets, which greatly improves the road segmentation accuracy and achieves good performance.
机译：遥感图像中的道路分割已广泛用于许多领域。基于深度学习的语义细分已成为道路分割的热门话题。随着卷积神经网络（CNN）结构的深化，卷积层中具有更多语义信息的特征对道路分割变得更加重要。然而，随着CNN网络的加热，卷积层的空间分辨率降低，这导致提取的道路失去一些重要的位置信息。为了解决这个问题，这封信提出了一种新的端到端道路分割方法，以有效利用不同层次的卷积层，以提高模型精确地感知道路边缘和形状的能力。该模型包括编码器和解码器。编码器对图像进行编码以获得不同级别和尺度的特征。解码器由两个模块组成：比例融合模块和比例敏感模块。在尺度融合模块中，汇集不同尺度的池层中的功能被融合以获得融合功能。在规模敏感模块中，学习网络末尾的重量张量以评估融合功能的重要性。这条路分割网络已经通过公共数据集进行了实验验证，这大大提高了道路分割精度并实现了良好的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

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