Land Cover Classification of PolSAR Images Using Semantic Segmentation Networks

被引：0

作者：

Turkmenli, Ilter ^{[1
]}

Aptoula, Erchan ^{[2
]}

Kayabol, Koray ^{[1
]}

机构：

[1] Gebze Tekn Univ, Elekt Muhendisligi Bolumu, Gebze, Turkey

[2] Gebze Tekn Univ, Bilisim Teknol Enst, Gebze, Turkey

来源：

2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2020年

关键词：

SAR image; land cover classification; Sentinel; 1; SegNet; U-net;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

With the free access to the SAR images obtained by the Sentinel 1 satellite, it provided the opportunity to make large scale land cover mapping using these images. This increased the need for fast and high-performance classification of large-scale SAR images. In this study, semantic segmentation networks such as SegNet and U-net have been proposed to obtain a fast and high performance classification. Also to train and test these recommended methods, a new data set consist of dual polarization SAR images of Turkey and ground truths has been created.

引用

页数：4

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