BI-DIRECTIONAL LSTM MODEL FOR CLASSIFICATION OF VEGETATION FROM SATELLITE TIME SERIES

被引：0

作者：

Bakhti, Khadidja ^{[1
,2
]}

Arabi, Mohammed El Amin ^{[1
]}

Chaib, Souleyman ^{[3
]}

Djerriri, Khelifa ^{[1
]}

Karoui, Moussa Sofiane ^{[1
]}

Boumaraf, Said ^{[2
]}

机构：

[1] Agence Spatiale Algerienne, Ctr Tech Spatiales, Arzew, Algeria

[2] Beijing Inst Technol, Beijing, Peoples R China

[3] Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China

来源：

2020 MEDITERRANEAN AND MIDDLE-EAST GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (M2GARSS) | 2020年

关键词：

land cover classification; vegetation modelling; recurrent neural network; long-short term memory; Bi-directional;

D O I：

10.1109/m2garss47143.2020.9105156

中图分类号：

S [农业科学];

学科分类号：

09 ;

摘要：

To further improve the classification accuracy of remote sensing time series data, in this paper, we propose a Bi-directional Long-Term and Short-Term Memory (denoted as BI-LSTM) based model for vegetation mapping and monitoring. The proposed model is applied to multi-temporal publicly available Sentinel-2A dataset with vegetation as the main theme. Experimental results have shown that the proposed approach has good performance in comparison with the state-of-the-art methods in term of accuracy, precision and recall. Moreover, it can efficiently use both past and future input features using the BI-LSTM component.

引用

页码：160 / 163

页数：4

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