The use of Synthetic Aperture Radar (SAR) has greatly advanced our capacity for comprehensive Earth monitoring, providing detailed insights into terrestrial surface use and cover regardless of weather conditions, and at any time of day or night. However, SAR imagery quality is often compromised by speckle, a granular disturbance that poses challenges in producing accurate results without suitable data processing. In this context, the present paper explores the cutting-edge application of Quantum Machine Learning (QML) in speckle filtering, harnessing quantum algorithms to address computational complexities. We introduce here QSpeckleFilter, a novel QML model for SAR speckle filtering. The proposed method compared to a previous work from the same authors showcases its superior performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on a testing dataset, and it opens new avenues for Earth Observation (EO) applications.

This work presents an unsupervised Deep Learning (DL)-based method for the matching of SAR images in case of high variability of the data. The method is based on an autoencoder trained in an unsupervised manner, with the SAR image in input and output given at the same time. From this architecture, the encoder is detached and used as feature extractor to obtain a set of feature maps at different depths to be used for the comparison. The correlation is applied to these feature maps to get the point of maximum correspondence among the image and the template to be found. The DL model has been trained with a heterogeneous dataset, with images coming from different SAR sources, in order to make it generalised and to make it work in a wide range of scenarios. The method has been tested both with similar and completely different images, showing good performances even in the most critical case.

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 for Land Cover Classification (LCC). 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 for EO data and LCC 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 (SOTA) and standard techniques for identifying volcanic eruptions chosen as a case study. Additionally, the method makes the model more resilient to dataset imbalance, a significant problem when training classical models. Lastly, the code is freely available so that interested researchers can reproduce and extend the results.

In this paper, an automatic processing chain, fully de-veloped in C++, for the generation of simplified Synthetic Aperture Radar (SAR) images of large urban scenarios is presented. The proposed method makes use of open GIS data and an open-source simulation technique based on ray tracing. The main novelties refer to: 1) the integration of a method for generating Three-Dimensional (3D) models of almost any arbitrary place on Earth, including in the model land cover information of the scene, 2) the possibility of setting the SAR parameters for the creation of images acquired under different geometries (e.g. satel-lite and airborne with arbitrary view (or look) angles), and 3) the ability to have at the end of the processing chain a geo-referenced image for comparisons with other types of data. The authors strongly believe that this pa-per can be of wide interest to researchers in the field of Remote Sensing (RS).

Cloud removal is a relevant topic in Remote Sensing, fostering medium- and high-resolution optical image usability for Earth monitoring and study. Recent applications of deep generative models and sequence-to-sequence-based models have proved their capability to advance the field significantly. Nevertheless, there are still some gaps: the amount of cloud coverage, the landscape temporal changes, and the density and thickness of clouds need further investigation. We fill some of these gaps in this work by introducing an innovative deep model. The proposed model is multi-modal, relying on both spatial and temporal sources of information to restore the whole optical scene of interest. We use the outcomes of both temporal-sequence blending and direct translation from Synthetic Aperture Radar (SAR) to optical images to obtain a pixel-wise restoration of the whole scene. The reconstructed images preserve scene details without resorting to a considerable portion of a clean image. Our approach’s advantage is demonstrated across various atmospheric conditions tested on different datasets. Quantitative and qualitative results prove that the proposed method obtains cloud-free images coping with landscape changes.

In this paper the authors present and validate a procedure for bridge monitoring, based on freely available satellite data and the straightforward Differential SAR Interferometry (DInSAR) technique. A displacement dataset of the Morandi bridge in Genoa (Italy) has been created, before its collapse. The outputs obtained were then compared to those found in literature and achieved through the Persistent Scatterer Interferometry (PSI), a more complex and reliable technique. Results demonstrate that the adopted procedure has great potentiality in the application field and could effectively be extended to different types of civil infrastructures.

In this paper, the authors aim to combine the latest state of the art models in image recognition with the best publicly available satellite images to create a system for landslide risk mitigation. We focus first on landslide detection and further propose a similar system to be used for prediction. Such models are valuable as they could easily be scaled up to provide data for hazard evaluation, as satellite imagery becomes increasingly available. The goal is to use satellite images and correlated data to enrich the public repository of data and guide disaster relief efforts for locating precise areas where landslides have occurred. Different image augmentation methods are used to increase diversity in the chosen dataset and create more robust classification. The resulting outputs are then fed into variants of 3-D convolutional neural networks. A review of the current literature indicates there is no research using CNNs (Convolutional Neural Networks) and freely available satellite imagery for classifying landslide risk. The model has shown to be ultimately able to achieve a significantly better than baseline accuracy.