• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Optical and quantum electronics >Hyperspectral image classification based on spectral and spatial information using ResNet with channel attention
【24h】

Hyperspectral image classification based on spectral and spatial information using ResNet with channel attention

机译：基于谱和空间信息的高光谱图像分类，使用reset与信道注意力

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

Classification of hyperspectral image (HSI) is widely used for the study of remotely sensed images. Convolutional Neural Networks (CNNs) are one of the most commonly chosen deep learning algorithms for visual data analysis. The HSI classification framework based on the CNN is presented in this paper. Since the imbalance between the high dimension of HSI input data and the limited amount of labeled training data would induce overfitting, current convolutional networks are fairly superficial for HSI classification. To stop the limited efficiency of feature learning, a new HSI classification network called Residual Spectral Spatial-Channel Attention Network (RSS-CAN) is proposed. By utilizing the "shortcut connection"' framework, RSS-CAN can use deeper layers to extract more succinct and efficient features. Furthermore, attention mechanism is used to emphasize meaningful features. In addition, we revised an HSI dataset called Shandong Feicheng. The resolution and pixel quantity of this dataset are significantly greater. In order to check its variety, it has been contrasted with state-of-the-art approaches. Experimental results with widely used hyperspectral image datasets demonstrate that, our proposed method has achieved better performance in comparison with state-of-the-art classifiers and conventional deep learning-based classifiers.
机译：高光谱图像（HSI）的分类广泛用于远程感测图像的研究。卷积神经网络（CNNS）是可视化数据分析最常用的深度学习算法之一。本文提出了基于CNN的HSI分类框架。由于HSI输入数据的高维度之间的不平衡和标记训练数据的有限量将引起过度拟合，因此当前的卷积网络对HSI分类相当肤浅。为了停止特征学习的有限效率，提出了一种新的HSI分类网络，称为残余光谱空间通道注意网络（RSS-CAN）。通过利用“快捷方式连接”框架，RSS-Crom可以使用更深入的层来提取更加简洁和有效的功能。此外，注意机制用于强调有意义的功能。此外，我们修改了一个名为山东飞城的HSI数据集。该数据集的分辨率和像素数量显着更大。为了检查其品种，它与最先进的方法形成鲜明对比。具有广泛使用的高光谱图像数据集的实验结果表明，与最先进的分类器和传统的基于深度学习的分类器相比，我们所提出的方法实现了更好的性能。

著录项

  • 来源
    《Optical and quantum electronics》 |2021年第3期|159.1-159.20|共20页
  • 作者

    Tural Abdulsamad; Fansheng Chen; Yiming Xue; Yueming Wang; Lin Yang; Dan Zeng;

  • 作者单位

    Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute of Advanced Communication and Data Science ShangDa road 99 Shanghai China;

    Key Laboratory of Intelligent Infrared Perception Chinese Academy of Sciences YuTian road 500 Shanghai 200083 China;

    Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute of Advanced Communication and Data Science ShangDa road 99 Shanghai China;

    Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences YuTian road 500 Shanghai 200083 China;

    Key Laboratory of Intelligent Infrared Perception Chinese Academy of Sciences YuTian road 500 Shanghai 200083 China;

    Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute of Advanced Communication and Data Science ShangDa road 99 Shanghai China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Hyperspectral image (HSI) classification; Shandong Feicheng dataset; Residual spectral spatial-channel attention network (RSS-CAN);

    机译：高光谱图像（HSI）分类;山东飞城数据集;剩余光谱空间通道注意网络（RSS-CAN）;

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