Research on Object Detection Technique in High Resolution Remote Sensing Images Based on U-Net

被引：0

作者：

Wu Zhihuan ^{[1
,2
]}

Gao Yongming ^{[1
]}

Li Lei ^{[1
]}

Fan Junliang ^{[1
]}

机构：

[1] Space Engn Univ, Beijing 100416, Peoples R China

[2] 63883 Troops, Luoyang 471000, Peoples R China

来源：

PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC) | 2018年

关键词：

object detection; remote Sensing; scene segmentation; convolutional neural networks;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Scene segmentation is an important method to implement fine-grained object detection in remote sensing image. The method based on fully convolution neural network is the hotspot in the field. In this paper, a hilly convolution neural network model based on U-Net model which achieves state of art result on biomedical segmentation application is proposed. Experiments on DSTL dataset show that the proposed model produces accurate pixel-wise classifications for remote sensing image in an end-to-end way.

引用

页码：2849 / 2853

页数：5

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