RIPARIAN ZONES CLASSIFICATION USING SATELLITE/UAV SYNERGY AND DEEP LEARNING

被引：0

作者：

Casagrande, Luan ^{[1
]}

Hirata, R., Jr. ^{[1
]}

机构：

[1] Univ Sao Paulo, Inst Math & Stat, Sao Paulo, Brazil

来源：

IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024 | 2024年

基金：

巴西圣保罗研究基金会;

关键词：

Deep learning; Classification; Calibration; Satellite and UAV synergy;

D O I：

10.1109/IGARSS53475.2024.10642449

中图分类号：

学科分类号：

摘要：

Riparian zones play a crucial role in water resources, wildlife, and communities. Governments have regulations to protect these areas, and quickly and accurately mapping vegetation near rivers to ascertain compliance with regulations is crucial. We propose the use of UAV data to calibrate a Sentinel-2 based model to predict class membership in riparian zones. In comparison to similar works, the proposed approach based on Convolutional Neural Network calibrated by a DeepLabV3+ is significantly better when evaluating the dominant class and has a higher potential to describe class membership for heterogeneous pixels.

引用

页码：4118 / 4122

页数：5

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