Lorenzo Bruzzone

• PhD
• University of Trento

The Jupiter Icy Moons Explorer (JUICE) is a European Space Agency mission to explore Jupiter and its three icy Galilean moons: Europa, Ganymede, and Callisto. Numerous JUICE investigations concern the magnetised space environments containing low-density populations of charged particles that surround each of these bodies. In the case of both Jupiter...

Unsupervised Change Detection (UCD) in multimodal Remote Sensing (RS) images remains a difficult challenge due to the inherent spatio-temporal complexity within data, and the heterogeneity arising from different imaging sensors. Inspired by recent advancements in Visual Foundation Models (VFMs) and Contrastive Learning (CL) methodologies, this rese...

In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust Change Detection (CD) on large volumes of Remote Sensing Images (RSIs). However, despite advances in CD methods, their practical application in real-world contexts remains limited due to the diverse input data and the appl...

In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust change detection (CD) on large volumes of remote sensing images (RSIs). However, despite advances in CD methods, their practical application in real-world contexts remains limited because of diverse input data and the appl...

Craters are the most typical geologic structures and landforms on the surface of Mars. Martian craters are widely distributed in a variety of morphology with multiple types and exhibit significant differences in scale. Many attempts have been made to automatic identification of Martian craters, yet existing methods do not satisfy the needs of large...

We present the state of the art on the study of surfaces and tenuous atmospheres of the icy Galilean satellites Ganymede, Europa and Callisto, from past and ongoing space exploration conducted with several spacecraft to recent telescopic observations, and we show how the ESA JUICE mission plans to explore these surfaces and atmospheres in detail wi...

Several potential subsurface openings have been observed on the surface of the Moon. These lunar pits are interesting in terms of science and for potential future habitation. However, it remains uncertain whether such pits provide access to cave conduits with extensive underground volumes. Here we analyse radar images of the Mare Tranquillitatis pi...

The JUpiter ICy moons Explorer (JUICE) of ESA was launched on 14 April 2023 and will arrive at Jupiter and its moons in July 2031. In this review article, we describe how JUICE will investigate the interior of the three icy Galilean moons, Ganymede, Callisto and Europa, during its Jupiter orbital tour and the final orbital phase around Ganymede. De...

The temporal consistency of yearly land-cover maps is of great importance to model the evolution and change of the land cover over the years. In this paper, we focus the attention on a novel approach to classification of yearly satellite image time series (SITS) that combines deep learning with Bayesian modelling, using Hidden Markov Models (HMMs)...

The Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) is a dual-frequency ice-penetrating radar (9 and 60 MHz) onboard the Europa Clipper mission. REASON is designed to probe Europa from exosphere to subsurface ocean, contributing the third dimension to observations of this enigmatic world. The hypotheses REASON will test are...

Spatiotemporal fusion aims to improve both the spatial and temporal resolution of remote sensing images, thus facilitating time-series analysis at a fine spatial scale. However, there are several important issues that limit the application of current spatiotemporal fusion methods. First, most spatiotemporal fusion methods are based on pixel-level c...

Landform classification and mapping of the Martian surface using Mars orbiter images can provide an important reference for landing site selection and rovers’ traversability evaluation in Mars exploration. Moreover, specific Martian landforms are closely associated with the evidences of water-related activities and Martian life, thus have crucial r...

With the increasing number of high-resolution (HR) images captured by various platforms, integrating spectral and spatial properties of data across different HR image types, such as multispectral (MS), hyperspectral (HS), and multitemporal (MT) images, remains a challenging task for object classification. This article proposes a novel hybrid framew...

In recent years, deep learning methods, in particular Convolutional Neural Networks (CNNs), have been increasingly used in Change Detection (CD). However, most CNN-based CD methods are primarily designed for analyzing only a single pair of images due to the challenge of collecting and constructing ground reference data during the system-training ph...

Radar sounder profiles are essential for imaging the subsurface of planetary bodies and the Earth as they provide valuable geological insights. However, the limited availability of high-resolution radargrams poses challenges. This paper proposes a novel method based on generative models to super-resolve radargrams. Our approach addresses the ill-po...

Pits are depressions in the ground that occur due to the collapse of the surface layer. The characterization from orbit of their internal structure using optical images is challenging due to uncontrolled illumination geometry. In this paper, we propose a methodology for the characterization of pits’ interiors by exploiting Synthetic Aperture Radar...

The mechanism of connecting multimodal signals through self-attention operation is a key factor in the success of multimodal Transformer networks in remote sensing data fusion tasks. However, traditional approaches assume access to all modalities during both training and inference, which can lead to severe degradation when dealing with modal-incomp...

High Resolution (HR) Satellite Image Time Series (SITS) are a valuable data source for analyzing Land Cover Change (LCC) due to their large amount of spatial, spectral, and temporal information. However, most existing LCC detection methods focus on binary Change Detection (CD) within a single year and fail to provide detailed information about the...

Deep Learning (DL) approaches are widely used to improve Change Detection (CD). In many application domains, unsupervised DL CD methods are preferred since gathering multi-temporal labeled samples is challenging. Many unsupervised CD methods use pre-trained DL models to extract multi-scale features. This does not allow for preserving the spatial-co...

Shallow aquifers are the primary water source to mitigate rising hydroclimatic fluctuations in arid areas, notably in North Africa and the Arabian Peninsula. The occurrence and dynamics of these expansive water-bodies remain poorly characterized due to the reliance on sporadic monitoring wells. To address this deficiency, several studies are explor...

The comprehension of pit craters on Venus’ surface is still limited both in terms of genetic process and geometric characteristics. Pit craters are valuable features for planetary scientists and geologists as they offer a window into a planetary body’s history, geological processes, and environmental conditions. In this context, morphometry is a va...

High-resolution Digital Terrain Models (DTMs) are critical for supporting planetary exploration missions and advancing scientific research. Recently, Deep Learning (DL) techniques have been applied to reconstruct high-resolution DTMs from single-view orbiter optical images, particularly for the Moon. However, DL-based methods face challenges in ret...

Lunar surface chemistry is essential for revealing petrological characteristics to understand the evolution of the Moon. Existing chemistry mapping from Apollo and Luna returned samples could only calibrate chemical features before 3.0 Gyr, missing the critical late period of the Moon. Here we present major oxides chemistry maps by adding distincti...

ESA’s Jupiter Icy Moons Explorer (JUICE) will provide a detailed investigation of the Jovian system in the 2030s, combining a suite of state-of-the-art instruments with an orbital tour tailored to maximise observing opportunities. We review the Jupiter science enabled by the JUICE mission, building on the legacy of discoveries from the Galileo, Cas...

The hydrological cycle is strongly influenced by the accumulation and melting of seasonal snow. For this reason, mountains are often claimed to be the “water towers” of the world. In this context, a key variable is the snow water equivalent (SWE). However, the complex processes of snow accumulation, redistribution, and ablation make its quantificat...

Modelling and large-scale mapping of forest aboveground biomass (AGB) is a complicated, challenging and expensive task. There are considerable variations in forest characteristics that creates functional disparity for different models and needs comprehensive evaluation. Moreover, the human-bias involved in the process of modelling and evaluation af...

Lava tubes are terrestrial tunnel-like natural subsurface caves. Mounting evidence suggests their presence on the Moon and Mars. Planetary radar sounders are nadir-looking instruments operating in the HF/VHF part of the spectrum with subsurface penetration capabilities. Recently, several studies either proposed future mission concepts for lava tube...

Multi-sensor data analysis allows exploiting heterogeneous data regularly acquired by the many available Remote Sensing (RS) systems. Machine- and deep-learning methods use the information of heterogeneous sources to improve the results obtained by using single-source data. However, the State-of-the-Art (SoA) methods analyze either the multi-scale...

Semantic segmentation is one of the most challenging tasks for very high resolution (VHR) remote sensing applications. Deep convolutional neural networks (CNN) based on the attention mechanism have shown outstanding performance in VHR remote sensing images semantic segmentation. However, existing attention-guided methods require the estimation of a...

This paper presents a novel system that produces multi-year high-resolution irrigation water demand maps for agricultural areas enabling a new level of detail for irrigation support for farmers and agricultural stakeholders. The system is based on a scalable distributed Deep Learning (DL) model trained on dense time series of Sentinel-2 images and...

With the increasing number of radar sounder (RS) instruments being used in planetary exploration, there is an increasing need for advanced and efficient RS data simulators. In this context, it is important to combine the advantages of the different simulators to produce end-to-end simulations at multiple scales in a reasonable time. This paper addr...

In recent years, numerous deep learning (DL)-based frameworks have been proposed for hyperspectral image classification (HSIC). Considering a large number of spectral bands of hyperspectral images (HSIs), it is still challenging to effectively utilize the spectral information and achieve accurate classification when few training samples are availab...

The exploration of Venus is increasingly gaining importance in the planetary science commu- nity. Recently, EnVision has been selected as the European Space Agency’s fifth Medium- class mission with a launch targeted in 2030. The subsurface radar sounder (SRS) instrument on board EnVision aims at profiling the shallow crust of Venus. The current ph...

The Radar for Icy Moon Exploration (RIME) on-board the JUICE mission will look for dielectric and mechanical interfaces below the icy crust of the Galilean moon Ganymede. Previous missions suggest that the surface of Ganymede is covered by two types of terrains, namely the dark terrain and bright terrain. The bright terrain covers two-thirds of the...

Deep Convolutional Neural Networks are state-of-the-art methods in the domain of classification of remote sensing (RS) data. However, traditional CNN models suffer from huge computational costs in learning land-use and land-cover features, particularly in large scale RS problems. To address this issue, we propose a reliable mono and dual Regulated...

Cet ouvrage traite des avancées en analyse des séries chronologiques d’images de télédétection par apprentissages statistique, automatique et/ou profond. Il présente un éventail de modèles mathématiques, de méthodes d’extraction d’informations spatio-temporelles et d’applications en observation de la Terre.Détection de changements et analyse des sé...

Cet ouvrage traite des avancées en analyse des séries chronologiques d’images de télédétection par apprentissages statistique, automatique et/ou profond. Il présente un éventail de modèles mathématiques, de méthodes d’extraction d’informations spatio-temporelles et d’applications en observation de la Terre.Détection de changements et analyse des sé...

Seasonal snow accumulation and release are so crucial for the hydrological cycle to the point that mountains have been claimed as the "water towers" of the world. A key variable in this sense is the snow water equivalent (SWE). However, the complex accumulation and snow redistribution processes render its quantification and prediction very challeng...

Change detection is a long-standing and challenging problem in remote sensing. Very often, features about changes are difficult to model beforehand, thus making the collection of changed samples a challenging task. In comparison, it is much easier to collect numerous no-change samples. It is possible to define a change detection approach by using o...

With more detailed spatial information being represented in very-high-resolution (VHR) remote sensing images, stringent requirements are imposed on accurate image classification. Due to the diverse land objects with intraclass variation and interclass similarity, efficient and fine classification of VHR images especially in complex scenes are chall...

Over recent decades, Change Detection (CD) has been intensively investigated due to the availability of High Resolution (HR) multi-spectral multi-temporal remote sensing images. Deep Learning (DL) based methods such as Convolutional Neural Network (CNN) have recently received increasing attention in CD problems demonstrating high potential. However...

Long-range contextual information is crucial for the semantic segmentation of High-Resolution (HR) Remote Sensing Images (RSIs). However, image cropping operations, commonly used for training neural networks, limit the perception of long-range contexts in large RSIs. To overcome this limitation, we propose a Wide-Context Network (WiCoNet) for the s...

Spectral unmixing aims at identifying the pure spectral signatures in hyperspectral images and simultaneously estimating their proportions in each pixel of the scene. By using an available spectral library as a dictionary, sparse-regression-based approaches aim at finding a subset of the dictionary that can optimally model each pixel in a given hyp...

The Danube Basin has been hit by several droughts in the last few years. As climate change makes weather extremes and temperature records in late winter and early spring more likely, water availability and irrigation possibilities become more important. In this paper, the crop water demand at field and national scale within the Danube Basin is pres...

This special issue of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) contains 11 papers both from the extended outcomes of the Multitemp 2019 presented papers, and from the submissions by following a general Call-for-Papers of this special issue. These papers focus on the interesting and relevant topic...

A comprehensive 3-D structural mapping of stem is essential for an accurate 3-D crown modeling and tree parameter estimation. Terrestrial laser scanning (TLS) is an effective technology for a comprehensive collection of individual tree level data, compared to destructive and costly field measurements. The performance of 3-D stem modeling techniques...

Availability of multitemporal (MT) images, such as the sentinel-2 (S2) ones, offers accurate spatial, spectral and temporal information to effectively monitor vegetation, more specifically agriculture. Agricultural practices can benefit from temporally dense satellite image time series (SITS) for accurate understanding of the phenological evolution...

Spaceborne radar sounders are high frequency (HF)/very high frequency (VHF) nadir-looking sensors devoted to subsurface investigations. Their data interpretation can be severely hindered by off-nadir surface clutter. Recent literature showed that the clutter suppression capabilities of this class of systems can be greatly enhanced by deploying an a...

Radar sounders (RS) are gaining importance in planetary missions thanks to their unique capability of providing direct measurements of subsurface structures. To support their design and data interpretation, several electromagnetic (e.m.) simulation techniques have been developed with enhanced capabilities of emulating the RS acquisition process. Ho...

This letter proposes a novel method based on Deep Learning (DL) to forest species classification in airborne Light Detection and Ranging (LiDAR) data. Differently from the state-of-the-art approaches, the proposed method: 1) does not assume any prior knowledge either on the forest to be classified or on the sensor used to acquire the LiDAR data and...

This letter presents a novel approach to tree-top detection in heterogeneous forest structures characterized by mixed species using high-density light detection and ranging (LiDAR) data. Although literature techniques can achieve accurate results in even-size and even-age homogeneous forests, they detect several false tree tops in forests character...

This paper presents an operational system for the automatic production of HR large-scale Land Cover (LC) maps in a fast, efficient and unsupervised manner. This is based on a scalable and parallelizable tile-based approach, which does not require the collection of new training data. The method leverages the complementary information provided by exi...

Among various multimodal remote sensing data, the pairing of multispectral (MS) and panchromatic (PAN) images is widely used in remote sensing applications. This article proposes a novel global collaborative fusion network (GCFnet) for joint classification of MS and PAN images. In particular, a global patch-free classification scheme based on an en...

Caves are one of the last frontiers of human exploration on Earth. They are very relevant scientific targets as they host significant biodiversity and unique geologic formations. The presence of underground passages accessible for human or robotic exploration are revealed by localized collapse of the near-surface ceiling of a cave system (skylight)...

This chapter aims to present a general mathematical framework for the representation and analysis of multispectral images. It introduces two statistical models for the description of the distribution of spectral difference-vectors, and provides from them change detection methods based on image difference. The chapter presents an overview of the cha...

Building extraction in VHR RSIs remains a challenging task due to occlusion and boundary ambiguity problems. Although conventional convolutional neural networks (CNNs) based methods are capable of exploiting local texture and context information, they fail to capture the shape patterns of buildings, which is a necessary constraint in the human reco...

Due to the scarcity of labeled samples, clustering in hyperspectral images (HSIs) has a great potential and application value. However, current clustering methods are mainly pixel-level techniques which neglect the large spectral variability of a scene, and suffer from massive time and memory consumption when dealing with large HSIs. In this paper,...

Remote sensing satellites have a great potential to recurrently monitor the dynamic changes of the Earth's surface in a wide geographical area, and contribute substantially to our current understanding of the land‐cover and land‐use changes. This chapter focuses on the unsupervised change detection (CD) problem in multitemporal multispectral images...

The effective combination of the complementary information provided by huge amount of unlabeled multisensor data (e.g., synthetic aperture radar (SAR) and optical images) is a critical issue in remote sensing. Recently, contrastive learning methods have reached remarkable success in obtaining meaningful feature representations from multiview data....

With the increasing availability and resolution of satellite sensor data, multispectral (MS) and panchromatic (PAN) images are the most popular data that are used in remote sensing among applications. This paper proposes a novel cross-resolution hidden layer features fusion (CRHFF) approach for joint classification of multi-resolution MS and PAN im...

During the last decades, radar sounders provided direct measurements (radargrams) of the Earth’s polar caps subsurface. Radargrams are of critical importance for a better understanding of glaciologic structures and processes of the ice sheet in the framework of climate change. This paper aims to automatically extract information on basal boundary c...

A general framework for change detection (CD) is proposed to analyze multi-modal remotely sensed data utilizing the Kronecker product between two data representations (vectors or matrices). The proposed method is sensor independent and provides comparable results to techniques that exist for specific sensors. The proposed fusion technique is a pixe...

The combination of data acquired by Landsat-8 and Sentinel-2 Earth Observation (EO) missions produces dense Time Series (TSs) of multispectral images that are essential for monitoring the dynamics of land-cover and land-use classes across the Earths surface with high temporal resolution. However, the optical sensors of the two missions have differe...

Research based on Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) data detected unusual radar bright basal reflections located at about 1.5 km depth in a Mars region denoted as Ultimi Scopuli. These reflections were interpreted as a signature of subglacial liquid water even though this interpretation is still being debated in th...

The current remote sensing (RS) open data policy for multispectral (MS) missions such as Sentinel-2 and Landsat-8, together with the availability of free cloud distributed processing platforms such as Google Earth Engine, makes it possible the quick generation of burned area (BA) products even for nonexperts in the field. Indeed, fires and BAs can...

Radar sounders (RS) play an important role in planetary investigation. However, the complex tasks of predicting performance and interpreting RS data and predicting performance require to perform data simulations. In the literature, there are different methods for RS data simulation, including: i) numerical methods involving the exact solution of Ma...

Passively sounding icy and rocky bodies in our solar system provides a way to observe the surface and subsurface of these objects without the need for costly transmitters. Jupiter's decametric radiation provides a suitable source of radio frequency signals for sounding on geological scales of interest, but its spectral structure can introduce undes...

The JUpiter Icy moons Explorer (JUICE) will investigate Ganymede's and Callisto's surfaces and subsurfaces from orbit to explore the geologic processes that have shaped and altered their surfaces by impact, tectonics, possible cryovolcanism, space weathering due to micrometeorites, radiation and charged particles as well as explore the structure an...

Bringing together a number of cutting-edge technologies that range from storing extremely large volumesof data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner, and having them operate over the same infrastructure poses unprecedentedchallenges. One of these challenges is the integration of Eur...

Semantic change detection (SCD) extends the multi-class change detection (MCD) task to provide not only the change locations but also the detailed land-cover/land-use (LCLU) categories before and after the observation intervals. This fine-grained semantic change information is very useful in many applications. Recent studies indicate that the SCD c...

High-resolution (HR) snow cover maps derived by remotely sensed images are an asset for data assimilation in hydrological models. However, the current satellite missions do not provide daily HR multispectral observations suitable for an accurate snow monitoring in alpine environments. On the contrary, low-resolution (LR) sensors acquire daily infor...