.png){kind=icon}
返回
SVM or deep learning? A comparative study on remote sensing image classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-016-2247-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With constant advancements in remote sensing technologies resulting in higher image resolution, there is a corresponding need to be able to mine useful data and information from remote sensing images. In this paper, we study auto-encoder (SAE) and support vector machine (SVM), and to examine their sensitivity, we include additional umber of training samples using the active learning frame. We then conduct a comparative evaluation. When classifying remote sensing images, SVM can also perform better than SAE in some circumstances, and active learning schemes can be used to achieve high classification accuracy in both methods.
Keyword:
Spatial big data
Sparse auto-encoder
Support vector machine
Active learning
Remote sensing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
.png){kind=icon}
分享收藏Automated Detection of Three-Dimensional Cars in Mobile Laser Scanning Point Clouds Using DBM-Hough-Forests基于dbm-hough-forests的移动激光扫描点云三维汽车自动检测
PREAI
分享收藏分享收藏
查看更多内容