...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Engineering Applications of Artificial Intelligence >Granular space, knowledge-encoded deep learning architecture and remote sensing image classification
【24h】

Granular space, knowledge-encoded deep learning architecture and remote sensing image classification

机译：颗粒状空间，知识编码的深度学习架构和遥感图像分类

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Hand-crafted features of remotely sensed (RS) image require the involvement of expensive human experts for classification. This factor motivates for designing the classification model with representative feature learning-based deep architecture to automate the feature extraction process and improve the generalization capability of the model. With this reasoning, we propose a deep auto-encoder neural network (NN) architecture with knowledge-encoded granular space for the classification of RS images. The network works with wavelet-rough granulated spaces and its architecture is designed with the encoded domain knowledge that strategically initializes the network parameters. Mostly, the learning time and performance of deep auto-encoders are persuaded by randomly selected weights and thus, we aim here to minimize these efforts with the domain knowledge. Neighborhood rough sets (NRS) are used to encode the domain knowledge and explore the contextual information for improved decision. We perform the knowledge-encoding operation for all stages of the auto-encoder. The proposed model thus exploits the mutual merits of deep network, wavelet-rough granular space and knowledge-encoding method. Comparative experimental results with multispectral and hyperspectral RS images demonstrate the superiority of our model to the related advanced methods.
机译：遥感（RS）图像的手工制作功能需要昂贵的人类专家参与分类。该因子用于设计具有基于特征学习的深度架构的分类模型，以自动化特征提取处理并提高模型的泛化能力。通过此推理，我们提出了一个深度自动编码的神经网络（NN）架构，具有知识编码的粒度空间，用于RS图像的分类。网络适​​用于小波粗糙的颗粒状空间，其架构旨在具有策略性初始化网络参数的编码域知识。大多数情况下，深度自动编码器的学习时间和性能被随机选择的权重说服，因此，我们的目标是为了使这些努力最小化域知识。邻域粗糙集（NRS）用于编码域知识并探索改进决策的上下文信息。我们对自动编码器的所有阶段执行知识编码操作。因此，所提出的模型利用深网络，小波粗糙粒度和知识编码方法的互补优点。具有多光谱和高光谱RS图像的比较实验结果证明了我们模型的优势与相关的先进方法。

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