Paolo Gamba

University of Pavia | UNIPV · Department of Electrical, Computer and Biomedical Engineering

Hyperspectral target detection aims to locate targets of interest in the scene, and deep learning-based detection methods have achieved the best results. However, black box network architectures are usually designed to directly learn the mapping between the original image and the discriminative features in a single data-driven manner, a choice that...

Accurate land cover information is pivotal in numerous planning and management activities. Synthetic Aperture Radar (SAR) data has emerged as a valuable resource for land cover assessment. Extracting scattering power components from Polarimetric SAR (PolSAR) data provides essential insights into the characteristics of land cover targets, aiding in...

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which of...

This paper presents a methodology designed to leverage multitemporal sequences of synthetic aperture radar (SAR) and multispectral data and automatically extract urban changes. The approach compares results using different radar and optical sensors, describing the advantages and drawbacks of using SAR data from the COnstellation of small Satellites...

Multi-task learning has been widely applied in visual learning to significantly enhance performances. The combination of hyperspectral change detection (HCD) and band reweighting can achieve discriminative feature enhancement for improving detection performance. However, existing multi-task models for these two tasks are unidirectional, with band r...

This study introduces a methodology for land cover mapping across extensive areas, utilizing multitemporal Sentinel-1 Synthetic Aperture Radar (SAR) data. The objective is to effectively process SAR data to extract spatio-temporal features that encapsulate temporal patterns within various land cover classes. The paper outlines the approach for proc...

Since you are reading this editorial, you are well aware that this journal is quite different than the other ones published by the IEEE Geoscience and Remote Sensing Society (GRSS). Indeed, it is a magazine, which implies that it has some specific features. In the past two issues I introduced how the technical contents of IEEE Geoscience and Remot...

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which of...

In line with what I did for the June issue, I will use my IEEE Geoscience and Remote Sensing Magazine ( GRSM ) editorial on one hand to summarize the content of the current issue and, on the other hand, to introduce a feature of this magazine that may not be well known to (or understood by) all our readers. Specifically, I will describe the poss...

A timely and accurate spatial mapping of built-up areas (BA) is crucial in making cities and human settlements safe, resilient, and sustainable. Synthetic Aperture Radar (SAR) data are useful for BA mapping due to strong coherent backscatter from diverse human-made targets, distinct texture patterns, and sensitivity to its geometric characteristics...

Precipitation is critical to human life, especially in developing nations like Ethiopia, where rain-fed agriculture supports the economy and food security. High variability and limited precipitation data are critical problems in the Awash River Basin. In this regards, high-resolution satellite-based precipitation estimation can fulfill these data g...

As you have already guessed from the title, and in line with my editorial in the March 2023 issue, I will use my space here to address two different points. First, the reader will find a summary of the contents of this issue, which is useful to those who would like to quickly navigate the issue and read only what they are interested in. The second...

Time series of remote sensing data has become an essential input for land use and land cover (LULC) studies. The current availability of multi-temporal data sets, from different sources and types, demands new classification approaches to explore their full capacity. In this study, we propose a non-parametric version of the Compound Maximum a poster...

Monitoring city boundaries and extents is a mandatory task so as to achieve the 11th Goal of sustainable development agenda 2030. This work aims at exploring the potential of the SAR dataset recorder by the SIASGE constellation for mapping urban areas and their extent. In particular, urban maps have been produced by applying the Urban EXTent algori...

p>In this work a methodology aimed at land cover mapping over geographically wide regions, leveraging multitemporal Sentinel-1 SAR data, is presented. The paper describes an effective way to process SAR multitemporal data in order to obtain a set of spatio-temporal features, which well-summarize the temporal patterns of different land cover classes...

p>In this work a methodology aimed at land cover mapping over geographically wide regions, leveraging multitemporal Sentinel-1 SAR data, is presented. The paper describes an effective way to process SAR multitemporal data in order to obtain a set of spatio-temporal features, which well-summarize the temporal patterns of different land cover classes...

Readers of this magazine know that the IEEE Geoscience and Remote Sensing Magazine ( GRSM ) editorial usually summarizes the content of the issue, providing hints to the interested researchers and practitioners to make it easier to find the articles or topics they are looking for. Since this is my first editorial, however, I will ask for your pa...

p>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...

p>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...

Uncontrolled and unsustainable urban sprawl are altering the Earth’s surface at unprecedented rates. This research explores the potential of active remote sensors for mapping urban areas, for monitoring urban expansion processes and for depicting landscape pattern dynamics in a metropolis of South America. Based on multi-temporal urban cover maps o...

Autoencoder (AE) has been widely used in the field of hyperspectral anomaly detection. It is assumed that the background can be reconstructed well, but the anomalies cannot. Hence, the pixels with larger reconstruction error are considered as anomalies. However, owing to the strong nonlinear representation ability of AE, it is difficult to distingu...

Hyperspectral unmixing is a crucial task in hyperspectral image processing and analysis. It aims to decompose mixed pixels into pure spectral signatures and their associated abundances. However, most current unmixing methods ignore the reality that the same pixel of a hyperspectral image has many different reflections simultaneously. To address thi...

Convolutional neural networks (CNNs) have a strong capacity to extract deep-level features from data. However, the standard convolution (SC) only considers the intensity-information and ignores the spatial gradient-information. Since spatial difference features are more robust to illumination invariance, this letter proposes a Multi-Scale Central D...

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 t...

The accuracy of hyperspectral target detection (HTD) is often affected by the problems of spectral variation and complex background distribution. Inspired by the powerful representational ability of deep learning, we proposed a 3-D convolution-based global spatial-spectral attention network (GS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xm...

Linear-based low-rank and sparse models (LRSM) and nonlinear-based deep autoencoder (DAE) model are proved to be effective for the task of anomaly detection (AD) in hyperspectral images (HSIs). The linear-based LRSM is self-explainable, while it may not characterize the complex scenes well. In contrast, the nonlinear-based DAE is able to extract th...

Spaceborne temporal sequences of Synthetic Aperture Radar (SAR) data have a definite advantage over multispectral data sequences in terms of continuity and regularity. Still, Deep Learning applications in remote sensing have primarily focused on multispectral data. This work is focused instead on a novel 3D Deep Learning architecture for SAR data s...

Spectral distortion severely limits detection performance in hyperspectral imagery, while feature learning with neural networks could provide sufficient capacity to enhance spectral consistency. This paper designs an end-to-end hyperspectral target detection (HTD) network based on transfer learning and nonlinear spectral synthesis (TLNSS). We first...

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 Gro...

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 Gro...

Hyperspectral imagery with very high spectral resolution provides a new insight for subtle nuances identification of similar substances. However, hyperspectral target detection faces significant challenges of intraclass dissimilarity and interclass similarity due to the unavoidable interference caused by atmosphere, illumination, and sensor noise....

Accurate and efficiently updated information on color-coated steel sheet (CCSS) roof materials in urban areas is of great significance for understanding the potential impact, challenges, and issues of these materials on urban sustainable development, human health, and the environment. Thanks to the development of Earth observation technologies, rem...

In recent years, machine learning algorithms have become widespread in all the fields of remote sensing and earth observation. 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 detec...

The strong urbanization impetus of developing countries leads to various urbanization phenomena such as building constructions, reconstructions and demolitions. It is desirable to monitor and recognize these intra-urban changes by utilizing temporal and spatial information in an automatic way. This may be useful, for example, to timely update urban...

Due to the side effects of asbestos on human health and environments, many countries have banned the use of asbestos-containing materials, but there are still illegal products with asbestos in daily life. In order to investigate the distributions of asbestos to facilitate its removal, this paper studies the feasibility of asbestos identification wi...

The binary hypothesis testing (BHT) is one of the most important models in hyperspectral target detection (HTD). However, this model is generally based on linear mixture model (LMM), and might be inaccurate to reflect target and background characterizations in some scenes. This paper presents a bilinear sparse target detector (BSTD) by applying bil...

Climate change studies require increasingly detailed information on land cover and land use, to precisely model and predict climate based on their status and changes. A fundamental land cover type that needs to be constantly monitored by the climate change community is water, but currently there is a lack of high-resolution water body maps at the g...

During the last twenty years, fast urbanization activities were highly concentrated in just few countries (e.g., China, India and Nigeria), and lead to the emergence of large urban aggregations, with high population density. Still, very few researches have focused on this dynamic phenomenon with a global perspective using multi-source remote sensin...

Urban areas are subject to multiple and very different changes, in a two- and three-dimensional sense, mostly as a consequence of human activities, such as urbanization, but also because of catastrophic and sudden events, such as earthquakes, landslides, or floods. This paper aims at designing a procedure able to cope with both types of changes by...

The vast amount of spectral information provided by hyperspectral images can be useful for different applications. However, the presence of redundant bands will negatively affect application performance. Therefore, it is crucial to select a relevant subset that preserves the information of the original set. In this paper, we present an automatic an...

This paper introduces a technique for using Recurrent Neural Networks to forecast Ae. aegypti mosquito (Dengue transmission vector) counts at neighbourhood-level, using Earth Observation data inputs as proxies to environmental variables. The model is validated using in situ data in two Brazilian cities, and compared with state-of-the-art multi-outp...

Although deep learning architectures are nowadays used in several research fields where automatized investigation of large scale datasets is required, the intrinsic mechanisms of deep learning networks are not fully understood yet. In this paper, a new approach for characterizing how information is processed within convolutional neural networks (CN...

Intra-body communication (IBC) is a novel key research area that will foster personalized medicine by allowing in situ and real time monitoring in daily life. In this work, the energy efficient galvanic coupling (GC) technology is used to send data through intra-body links. A novel sound card-based GC testbed is designed and implemented, whose main...

Hyperspectral unmixing is an important problem for remotely sensed data interpretation. It amounts at estimating the spectral signatures of the pure spectral constituents in the scene (endmembers) and their corresponding subpixel fractional abundances. Although the unmixing problem is inherently nonlinear (due to multiple scattering), the nonlinear...

As a semi-enclosed crater basin on the northwest rim of Imbrium basin, Sinus Iridum is a key site to investigate the geological characteristics at the intersection of two basins. For this reason, we focused on model age determination in Sinus Iridum basin using Chang’E−2 high-resolution images coupled with compositional maps from the Clementine dat...

Presents the President’s message for this issue of the publication.

Mosquitoes propagate many human diseases, some widespread and with no vaccines. The Ae. aegypti mosquito vector transmits Zika, Chikungunya, and Dengue viruses. Effective public health interventions to control the spread of these diseases and protect the population require models that explain the core environmental drivers of the vector population....

In the last few decades, urbanization activities have promoted the emergence of megacities, megalopolis, urban clusters or large urban aggregations, but only a few studies have analyzed them using remote sensing data in both the spatial and the temporal domains. In this paper, combining SAR and multispectral sensors with different resolutions, a no...

This study presents a new scheme to extract impervious surface area from synthetic-aperture radar (SAR) images exploiting auxiliary user-generated content (UGC). The presented scheme includes the automatic generation of training samples based on the combination of UGC and SAR data, and SAR data pre-processing, leading to impervious surface area cla...

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Although multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth's surface, nonidealities and estimation imperfections between records and investigation models can limit its actual information extraction ability. In this article, we aim at predicting the maximum information extraction that ca...

Man-made impervious surfaces, indicating the human footprint on Earth, are an environmental concern because it leads to a chain of events that modifies urban air and water resources. To better map man-made impervious surfaces in any region of interest (ROI), we propose a framework for learning to map impervious areas in any ROIs from Sentinel-2 ima...

Man-made impervious surfaces, indicating the human footprint on Earth, are an environmental concern because it leads to a chain of events that modifies urban air and water resources. To better map man-made impervious surfaces in any region of interest (ROI), we propose a framework for learning to map impervious areas in any ROIs from Sentinel-2 ima...

Extensive attention has been widely paid to enhance the spatial resolution of hyperspectral (HS) images with the aid of multispectral (MS) images in remote sensing. However, the ability in the fusion of HS and MS images remains to be improved, particularly in large-scale scenes, due to the limited acquisition of HS images. Alternatively, we super-r...

Extensive attention has been widely paid to enhance the spatial resolution of hyperspectral (HS) images with the aid of multispectral (MS) images in remote sensing. However, the ability in the fusion of HS and MS images remains to be improved, particularly in large-scale scenes, due to the limited acquisition of HS images. Alternatively, we super-r...

Satellite images from the same scene observed over time can be composed in an image stack, which could be modeled as a 3-D cube. To handle this type of remote sensing data, on the one side, unidimensional dynamical models have been considered, modeling each pixel separately along the time (pixel-based approach), and exploring the temporal correlati...

Building exposure and vulnerability models for seismic risk assessment have been the focus of a number of European projects in recent years, but there has never been a concerted effort among the research community to produce a uniform European risk model. The European Commission’s Horizon 2020 SERA project has a work package that is dedicated to th...

The area, distribution, and temporal dynamics of anthropogenic impervious surface (AIS) at large scale are significant for environmental, ecological and socio-economic studies. Remote sensing has become an important tool for monitoring large scale AIS, while it remains challenging for accurate extraction of AIS using optical datasets alone due to t...

Presents the President’s message for this issue of the publication.

Presents the President’s message for this issue of the publication.

This article introduces the Rayleigh autoregressive moving average (RARMA) model, which is useful to interpret multiple different sets of remotely sensed data, from wind measurements to multitemporal synthetic aperture radar (SAR) sequences. The RARMA model is indeed suitable for continuous, asymmetric, and nonnegative signals observed over time. I...

Presents the President’s message for this issue of the publication.

Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidit...

Intra-Body Communication (IBC) is an emerging research area that will transform the personalized medicine by allowing real time and in situ monitoring in daily life. A galvanic coupling (GC) technology is used in this work to send data through weak currents for intra-body links, as an energy efficient alternative to the current radio frequency (RF)...

The papers in this special section examine the use of remote sensing technology to promote environmental sustainability in Asia-Pacific regions. Worldwide urbanization and deforestation are the two main interconnected ways that human activities are continually changing and reshaping the earth’s surface. How earth observation and remote sensing tech...

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Modern wireless sensor networks (WSNs) for Internet of Things (IoT) applications require low-complexity algorithms for positioning, due to the large number of nodes with low power consumption. Thus, simple received signal strength indicator (RSSI) based ranging techniques represent an attractive option for low power systems such as LoRa ones. Howev...

This paper illustrates a fusion approach to jointly exploit Sentinel-1 (S1) and Sentinel-2 (S2) data to detect urban areas. The proposed procedure is designed to automatically and effectively exploit the specific characteristics of synthetic aperture radar (SAR) and multispectral data, so that it can be safely applied to different urban environment...

Built-up (BU) area extraction from remote sensing images is important to monitor and manage urbanization and industrialization. In this letter, we propose two BU area extraction techniques based on the analysis of fully polarimetric synthetic aperture radar (PolSAR) data. Both methods exploit the geodesic distance on the unit sphere in the space of...

Presents the President's message for this issue of the publication.

The rapid expansion of cities globally leads to new challenges related to quality of life and health. The presence and fractional distribution of vegetation within urban cities directly impact the life and health of urban dwellers. This paper presents an approach to map urban vegetation from Sentinel-2 data. The twin Sentinel-2 satellites offer a 5...

This paper reviews in detail the contributions of hyperspectral imaging to the topic of urban remote sensing. Hyperspectral imaging is traditionally connected to the spectral characterization of surface materials. Moreover, urban areas are characterized by a very complex geometrical structure, which requires either very high spatial resolution or c...