Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationships

TL;DR

This paper introduces a deep learning framework that effectively combines radar and optical satellite time series data for land cover mapping, utilizing a specialized RNN with attention and domain-guided pretraining.

Contribution

It presents a novel RNN-based architecture with a custom attention mechanism and a domain expert-guided pretraining strategy for multi-source land cover classification.

Findings

The proposed model outperforms several machine learning competitors.

The attention mechanism improves the exploitation of multi-source data.

Domain expert knowledge enhances model initialization and performance.

Abstract

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at highspatial resolution and high revisit time, respectively, radar and optical imagesthat support a wide range of Earth surface monitoring tasks such as LandUse/Land Cover mapping. A long-standing challenge in the remote sensingcommunity is about how to efficiently exploit multiple sources of information and leverage their complementary. In this particular case, get the most out ofradar and optical satellite image time series (SITS). Here, we propose to dealwith land cover mapping through a deep learning framework especially tailoredto leverage the multi-source complementarity provided by radar and opticalSITS. The proposed architecture is based on an extension of Recurrent NeuralNetwork (RNN) enriched via a customized attention mechanism capable to fitthe specificity of SITS data. In addition, we propose a new pretraining strategythat exploits domain expert knowledge to guide the model parameter initial-ization. Thorough experimental evaluations involving several machine learningcompetitors, on two contrasted study sites, have demonstrated the suitabilityof our new attention mechanism combined with the extend RNN model as wellas the benefit/limit to inject domain expert knowledge in the neural networktraining process.