Building detection by local region features in SAR images

被引：2

作者：

Ye, S. P. ^{[1
,2
]}

Chen, C. X. ^{[1
]}

Nedzved, A. ^{[3
]}

Jiang, J. ^{[1
,4
]}

机构：

[1] Zhejiang Shuren Univ, Coll Informat Sci & Technol, Hangzhou, Zhejiang, Peoples R China

[2] Zhejiang Univ, Sch Earth Sci, Hangzhou, Zhejiang, Peoples R China

[3] Belarusian State Univ, Dept Comp Applicat & Syst, Minsk, BELARUS

[4] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou, Zhejiang, Peoples R China

来源：

COMPUTER OPTICS | 2020年 / 44卷 / 06期

关键词：

SAR images; building detection; YOLO network;

D O I：

10.18287/2412-6179-CO-703

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

The buildings are very complex for detection on SAR images, where the basic features of those are shadows. There are many different representations for SAR shadow. As result it is no possible to use convolutional neural network for building detection directly. In this article we give property analysis of SAR shadows of different type buildings. After that, each region (ROI) prepared for training of building detection is corrected with its own SAR shadow properties. Reconstructions of ROI will be put in a modified YOLO network for building detection with better quality result.

引用

页码：944 / 950

页数：7

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