FLOOD AREA SEGMENTATION WITH ALOS-2/PALSAR-2 INTENSITY AND INTERFEROMETRIC COHERENCE WITH DEEP LEARNING

被引：0

作者：

Tanaka, Kentaro ^{[1
]}

Uchida, Shota ^{[1
]}

Yasui, Syusuke ^{[1
]}

Deguchi, Tomonori ^{[1
]}

Saito, Ryo ^{[1
]}

Jaturapitpornchai, Raveerat ^{[1
,2
]}

机构：

[1] Space Shift Inc, I-6-1 Otemachi, Tokyo, Japan

[2] Ist Italiano Tecnol, Ctr Cultural Heritage Technol, I-30170 Venice, Italy

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

Deep learning; UNet plus; Flood detection; SAR image; Interferometric coherence;

D O I：

10.1109/IGARSS52108.2023.10281423

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Modern deep learning techniques have the potential to accurately predict the extent of flooding over a wide area in near real-time from SAR satellite image. In this paper, we use U- Net and UNet++ to detect flood area from a pair of PALSAR- 2 images acquired in the same area at two different stages of flooding and non-flooding. We propose adding interferometric coherence to SAR intensity for the training set of deep learning network. The results show improved accuracy and distribution of flood mapping compared with those using only intensity images.

引用

页码：700 / 703

页数：4

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