• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>SPIE remote sensing conference >Object-oriented crops classification for remote sensing images based on convolutional neural network
【24h】

Object-oriented crops classification for remote sensing images based on convolutional neural network

机译：基于卷积神经网络的面向对象农作物遥感图像分类

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

摘要

Deep learning technology such as convolutional neural networks (CNN) has achieved outstanding results in the field of crops classification for remote sensing images. The way of land cover or crop types remote sensing classification using CNN is mainly pixel-based classification which is often affected by the phenomenon of "salt and pepper". In order to reduce this effect, an object-oriented crops classification method based on CNN is proposed in this paper. By combining image segmentation technology and CNN model, we use this method to obtain the results of crops classification from Sentinel-2A multi-spectral remote sensing images in Yuanyang County, Henan Province, China. The experiment show that, compared with the pixel level classification based on CNN which only consider the spectral and temporal characteristics of the crops, the method we proposed comprehensively utilizes more detailed information such as spectral feature, texture feature, spatial relationship, and color space. Thus, it gains a better discriminability for some specific crop and achieves higher classification accuracy.
机译：诸如卷积神经网络（CNN）之类的深度学习技术在用于遥感图像的农作物分类领域中取得了出色的成绩。使用CNN进行土地覆盖或作物类型遥感分类的方法主要是基于像素的分类，通常受“盐和胡椒”现象的影响。为了减轻这种影响，提出了一种基于CNN的面向对象农作物分类方法。通过将图像分割技术和CNN模型相结合，我们使用这种方法从中国河南省元阳县的Sentinel-2A多光谱遥感图像中获得了作物分类的结果。实验表明，与仅考虑农作物的光谱和时间特征的基于CNN的像素级分类相比，我们提出的方法综合利用了光谱特征，纹理特征，空间关系和色彩空间等更详细的信息。因此，它对于某些特定作物具有更好的可分辨性，并实现了更高的分类精度。

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