The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring

Pignatti, Stefano; Palombo, Angelo; Pascucci, Simone; Romano, Filomena; Santini, Federico; Simoniello, T.; Amato, Umberto; Cuomo, Vincenzo; Acito, N.; Diani, Marco; Matteoli, Stefania; Corsini, Giovanni; Casa, Raffaele; Roberto, De Bonis; Laneve, Giovanni; Ananasso, Cristina

doi:10.1109/igarss.2013.6723850

Cited by 58 publications

(32 citation statements)

References 4 publications

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“…Furthermore, field calibration of the data can greatly increase the quality of the data also allowing deeper explanations of the physical relationships between spectral signal and slope stability. Future hyperspectral satellite missions like EnMap and PRISMA will allow even more precise mineral mapping analyses (Pignatti et al 2013, Guanter et al 2015.…”

Section: Discussionmentioning

confidence: 99%

Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north‐eastern Sicily, Italy)

Lombardo

Bachofer

Cama

et al. 2016

Earth Surf Processes Landf

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This study aims at evaluating the performance of the Maximum Entropy method in assessing landslide susceptibility, exploiting topographic and multispectral remote sensing predictors. We selected the catchment of the Giampilieri stream, which is located in the north‐eastern sector of Sicily (southern Italy), as test site. On 1 October 2009, a storm rainfall triggered in this area hundreds of debris flow/avalanche phenomena causing extensive economical damage and loss of life. Within this area a presence‐only‐based statistical method was applied to obtain susceptibility models capable of distinguishing future activation sites of debris flow and debris slide, which where the main source of failure mechanisms for flow or avalanche type propagation. The set of predictors used in this experiment comprised primary and secondary topographic attributes, derived by processing a high resolution digital elevation model, CORINE land cover data and a set of vegetation and mineral indices obtained by processing multispectral ASTER images. All the selected data sources are dated before the disaster. A spatially random partition technique was adopted for validation, generating 50 replicates for each of the two considered movement typologies in order to assess accuracy, precision and reliability of the models. The debris slide and debris flow susceptibility models produced high performances with the first type being the best fit. The evaluation of the probability estimates around the mean value for each mapped pixel shows an inverted relation, with the most robust models corresponding to the debris flows. With respect to the role of each predictor within the modelling phase, debris flows appeared to be primarily controlled by topographic attributes whilst the debris slides were better explained by remotely sensed derived indices, particularly by the occurrence of previous wildfires across the slope. The overall excellent performances of the two models suggest promising perspectives for the application of presence‐only methods and remote sensing derived predictors. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north‐eastern Sicily, Italy)

Lombardo

Bachofer

Cama

et al. 2016

Earth Surf Processes Landf

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“…This special issue provides an overview of the EnMAP mission and ongoing research conducted in the scientific preparation of the mission y. EnMAP and other upcoming imaging spectroscopy missions, such as HyspIRI [25], HISUI [26], PRISMA [27], HYPXIM [28], and SHALOM [29] will deliver valuable information for various environmental applications towards a better understanding of ecosystem processes and sustainable resource management. When operating in parallel, these missions will allow for an increased spatial coverage and acquisition frequency of hyperspectral spaceborne imagery.…”

Section: Discussionmentioning

confidence: 99%

Preface: The Environmental Mapping and Analysis Program (EnMAP) Mission: Preparing for Its Scientific Exploitation

et al. 2016

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Abstract:The imaging spectroscopy mission EnMAP aims to assess the state and evolution of terrestrial and aquatic ecosystems, examine the multifaceted impacts of human activities, and support a sustainable use of natural resources. Once in operation (scheduled to launch in 2019), EnMAP will provide high-quality observations in the visible to near-infrared and shortwave-infrared spectral range. The scientific preparation of the mission comprises an extensive science program. This special issue presents a collection of research articles, demonstrating the potential of EnMAP for various applications along with overview articles on the mission and software tools developed within its scientific preparation.

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“…In particular, imaging spectroscopy (also known as hyperspectral imaging) [1] has attracted the attention of many researchers because of the great potential of hyperspectral images (HSIs) in characterizing the surface of the Earth by covering the visible, near-infrared and shortwave infrared regions of the electromagnetic spectrum. To exploit these data, multiple EO missions are now using imaging spectrometers, such as the Environmental Mapping and Analysis Programme (EnMAP) [2,3], or the Hyperspectral Precursor of the Application Mission (PRISMA) [4]. In this context, a high-dimensional stream of remotely sensed HSI data is now being generated, which can be applied to multiple tasks after being properly processed, such as managing of natural and environmental resources, urban planning and monitoring of agricultural fields, risk prevention and natural/human disaster management, among others.…”

Section: Introductionmentioning

confidence: 99%

A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification

et al. 2020

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The storage and processing of remotely sensed hyperspectral images (HSIs) is facing important challenges due to the computational requirements involved in the analysis of these images, characterized by continuous and narrow spectral channels. Although HSIs offer many opportunities for accurately modeling and mapping the surface of the Earth in a wide range of applications, they comprise massive data cubes. These huge amounts of data impose important requirements from the storage and processing points of view. The support vector machine (SVM) has been one of the most powerful machine learning classifiers, able to process HSI data without applying previous feature extraction steps, exhibiting a robust behaviour with high dimensional data and obtaining high classification accuracies. Nevertheless, the training and prediction stages of this supervised classifier are very time-consuming, especially for large and complex problems that require an intensive use of memory and computational resources. This paper develops a new, highly efficient implementation of SVMs that exploits the high computational power of graphics processing units (GPUs) to reduce the execution time by massively parallelizing the operations of the algorithm while performing efficient memory management during data-reading and writing instructions. Our experiments, conducted over different HSI benchmarks, demonstrate the efficiency of our GPU implementation.

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The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring

Cited by 58 publications

References 4 publications

Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north‐eastern Sicily, Italy)

Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north‐eastern Sicily, Italy)

Preface: The Environmental Mapping and Analysis Program (EnMAP) Mission: Preparing for Its Scientific Exploitation

A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification

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