Quantum Machine Learning (QML) is an emerging technology that only recently has begun to take root in the research fields of Earth Observation (EO) and Remote Sensing (RS),  and whose state of the art is roughly divided into one group oriented to fully quantum solutions, and in another oriented to hybrid solutions.  Very few works applied QML to EO tasks, and none of them explored a methodology able to give guidelines on the hyperparameter tuning of the quantum part. As a first step in the direction of quantum advantage for RS data classification, this letter opens new research lines, allowing us to demonstrate that there are more convenient solutions to simply increasing the number of qubits in the quantum part. To pave the first steps for researchers interested in the above, the structure of a new hybrid quantum neural network is proposed with a strategy to choose the number of qubits to find the most efficient combination in terms of both system complexity and results accuracy. We sampled and tried a number of configurations, and using the suggested method we came up with the most efficient solution (in terms of the selected metrics). Better performance is achieved with less model complexity when tested and compared with state-of-the-art techniques for identifying volcanic eruptions  chosen as a case study. Additionally, the method makes the model more resilient to dataset imbalance, a major problem when training classical models. Lastly, the code is freely available so that interested researchers can  reproduce and extend the results.

In recent years, Machine Learning (ML) algorithms have become widespread in all the fields of Remote Sensing (RS) and Earth Observation (EO). This has allowed the rapid development of new procedures to solve problems affecting these sectors. In this context, this work aims at presenting a novel method for filtering speckle noise from Sentinel-1 Ground Range Detected (GRD) data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs). The paper provides an easy yet very effective approach to extract the large amount of training data needed for DL approaches in this challenging case. The experimental results on simulated speckled images and an actual SAR dataset show a clear improvement with respect to the state of the art in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Equivalent Number of Looks (ENL), proving the effectiveness of the proposed architecture.