| 来源类型 | Peer-reviewed Article |
| 规范类型 | 其他 |
| Using convolutional neural networks to identify objects from very high-resolution remote sensing imagery | |
| Brian JOHNSON | |
| 发表日期 | 2018-05 |
| 出版者 | SPIE |
| 出版年 | 2018 |
| 页码 | 025010 |
| 语种 | 英语 |
| 概述 | A new method for classifying land cover features in high resolution satellite imagery was developed. The method involves (1) performing image segmentation to delineate homogeneous... |
| 摘要 | A new method for classifying land cover features in high resolution satellite imagery was developed. The method involves (1) performing image segmentation to delineate homogeneous ground objects in the image, and (2) using a convolutional neural network classifier to extract the land cover of the segments (i.e. whether they represent buildings, cropland, forest, etc.). |
| 主题 | Resilient Livelihoods$Sustainable Cities & Societies |
| URL | https://pub.iges.or.jp/pub/using-convolutional-neural-networks-identify |
| 来源智库 | Institute for Global Environmental Strategies (Japan) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/311766 |
| 推荐引用方式 GB/T 7714 | Brian JOHNSON. Using convolutional neural networks to identify objects from very high-resolution remote sensing imagery. 2018. |
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