Multisensor Temporal Unsupervised Domain Adaptation for Land Cover Mapping With Spatial Pseudo-Labeling and Adversarial Learning

Abstract

With the huge variety of earth observation satellite missions available nowadays, the collection of multi-sensor remote sensing information depicting the same geographical area has become systematic in practice, paving the way to the further breakthroughs in automatic land cover mapping with the aim to support decision makers in a variety of land management applications. In this context, along with the increase in the volume of data available, the availability of ground truth data to train supervised models, which is usually time-consuming and costly, may even be more critical. In this scenario, the possibility to transfer a model learnt on a particular time span ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source domain</i> ) to a different period of time ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target domain</i> ), over the same geographical area, can be advantageous in terms of both cost and time efforts. However, such model transfer is challenging due to different climate, weather or environmental conditions affecting remote sensing data collected at different time periods, resulting in possible distribution shifts between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target</i> domains. With the aim to cope with the multi-sensor temporal transfer scenario in the context of land cover mapping, where multi-temporal and multi-scale information are used jointly, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M³SPADA</i> (Multi-sensor, Multi-temporal and Multi-scale SPatially-Aware Domain Adaptation framework), a deep learning methodology that jointly exploits self-training and adversarial learning to transfer a multi-sensor land cover classifier from a time period (year) to a different one on the same geographical area. Here, we consider the case in which each domain (source and target) is described by a pair of remote sensing data sets: a satellite image time series (SITS) of optical images and a single Very High spatial Resolution (VHR) scene. Experimental evaluation on a real-world study case located in Burkina Faso and characterized by operational constraints shows the quality of our proposal to deal with the temporal multi-sensor transfer in the context of land cover mapping.

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