DEEP CONVOLUTIONAL NEURAL NETWORK FOR MANGROVE MAPPING

被引：9

作者：

Iovan, Corina ^{[1
]}

Kulbicki, Michel ^{[1
]}

Mermet, Eric ^{[2
]}

机构：

[1] IRD, Lab Excellence CORAIL, UMR Entropie, BPA5, Noumea 98848, New Caledonia

[2] EHESS, CAMS, UMR 8557, 54 Bd Raspail, F-75006 Paris, France

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

关键词：

CNN; mangroves; Sentinel-2; World-View2; remote sensing; FORESTS; CLASSIFICATION;

D O I：

10.1109/IGARSS39084.2020.9323802

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Updated information on the spatial distribution of mangrove forests is of high importance for management plans. Yet, access to mangrove distribution maps is limited, even-though remote sensing data is currently freely available and deep learning algorithms score high performances in automatic classification tasks. The methodologies developed in this paper are based on a deep convolutional neural network and have been tested on WorldView 2 and Sentinel-2 images. The obtained results are highly satisfactory and open perspectives for automatically mapping mangrove distribution over large areas.

引用

页码：1969 / 1972

页数：4

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