Prisma Hyperspectral Mission Products

Guarini, Rocchina; Loizzo, R.; Facchinetti, Claudia; Longo, F.; Ponticelli, Beatrice; Faraci, Marco; Dami, Michele; Cosi, M.; Amoruso, Leonardo; Pasquale, Vito De; Taggio, Nicolò; Santoro, Francesca; Colandrea, P.; Miotti, Efer; Nicolantonio, W. Di

doi:10.1109/igarss.2018.8517785

Cited by 31 publications

(16 citation statements)

References 1 publication

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“…Launched on 22 March 2019, the PRISMA pushbroom sensor has 239 hyperspectral bands in a spectral range between 400-2500 nm with 30 m of spatial resolution (5 m for Panchromatic band), and a revisit time of approximately 29 days. Of the 239 bands, 66 correspond to Visible and Near Infrared region (VNIR: 400-1010 nm) and 173 to the Short Wave Infrared region (SWIR: 920-2505 nm) spectrum 1,11 . A product collected on 05/04/2020, with a cloud coverage of 3,27% was downloaded from the PRISMA Mission Catalog website (https://prisma.asi.it/).…”

Section: Data and Preprocessingmentioning

confidence: 99%

The potential of spectral unmixing method applied to PRISMA hyperspectral images in the identification of Li minerals: an evaluation for prospecting purposes

Santos

Cardoso-Fernandes

Lima³

et al. 2022

Earth Resources and Environmental Remote Sensing/Gis Applications XIII

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Different remote sensing methods already applied have proven efficient in identifying pegmatites, but the high number of the false positives and the size of the study areas involved, make the location of new points of interest for exploration a difficult task. In order to develop and evaluate more autonomous tools for localization of new points of interest, this study aims to apply the Envi Spectral Hourglass Wizard (SHW) algorithm and spectral analysis, both applied on PRISMA hyperspectral images, to determine mineral distribution in St. Austell greisen deposit, a Li exploration target located at Cornwall, UK. The SHW finds endmembers within the dataset to map their location and sub-pixel abundance. This processing workflow is composed of several steps: (i) MNF (Minimum Noise Fraction) Transform; (ii) PPI (Pixel Purity Index); (iii) n-D space visualizer, allowing the extraction of the endmembers and; (iv) the SAM (Spectral Angle Mapper) classification algorithm, which classifies the image creating a class for each collected endmember. The classification results show the potential of the method to indicate the presence of Li minerals being able to identify Kaolinite and map the distribution and abundance of Topaz, Tourmaline and Biotite. This approach is highly valuable for the Li mining industry.

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Section: Data and Preprocessingmentioning

confidence: 99%

The potential of spectral unmixing method applied to PRISMA hyperspectral images in the identification of Li minerals: an evaluation for prospecting purposes

Santos

Cardoso-Fernandes

Lima³

et al. 2022

Earth Resources and Environmental Remote Sensing/Gis Applications XIII

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“…PRISMA is a medium-resolution hyperspectral imaging mission of the Italian Space Agency that was launched in March 2019 [74]. The PRISMA sensor is a spaceborne system that acquires hyperspectral datasets continuously, with a repeat orbital cycle of approximately 29 days [75].…”

Section: Prisma Imagesmentioning

confidence: 99%

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

2021

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Wildfires are one of the most destructive natural disasters that can affect our environment, with significant effects also on wildlife. Recently, climate change and human activities have resulted in higher frequencies of wildfires throughout the world. Timely and accurate detection of the burned areas can help to make decisions for their management. Remote sensing satellite imagery can have a key role in mapping burned areas due to its wide coverage, high-resolution data collection, and low capture times. However, although many studies have reported on burned area mapping based on remote sensing imagery in recent decades, accurate burned area mapping remains a major challenge due to the complexity of the background and the diversity of the burned areas. This paper presents a novel framework for burned area mapping based on Deep Siamese Morphological Neural Network (DSMNN-Net) and heterogeneous datasets. The DSMNN-Net framework is based on change detection through proposing a pre/post-fire method that is compatible with heterogeneous remote sensing datasets. The proposed network combines multiscale convolution layers and morphological layers (erosion and dilation) to generate deep features. To evaluate the performance of the method proposed here, two case study areas in Australian forests were selected. The framework used can better detect burned areas compared to other state-of-the-art burned area mapping procedures, with a performance of >98% for overall accuracy index, and a kappa coefficient of >0.9, using multispectral Sentinel-2 and hyperspectral PRISMA image datasets. The analyses of the two datasets illustrate that the DSMNN-Net is sufficiently valid and robust for burned area mapping, and especially for complex areas.

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“…The PRISMA data are made available for free by the Italian Space Agency (ASI) [23,28]. The data are delivered in the HDF5 format with four options: (1) Level 1 (Hyperspectral/PAN) is radiometrically corrected and calibrated TOA (top of atmosphere) data;…”

Section: Prisma Scenementioning

confidence: 99%

“…We used the LC1-PRISMA product acquired on 27 December 2019. The image processing was implemented by using the ENVI 5.5 (Harris Geospatial Solution, USA) software and consisted of three steps: (1) applying the scale factor 100 to convert the digital numbers of the L1 product to radiance expressed in Wm −2 sr −1 µm −1 [23,28]; (2) removing noisy bands and building the hyperspectral cube, by stacking the visible and SWIR data; (3) implementing the atmospheric correction and obtaining a reflectance spectrum; (4) producing spectral detection maps and implementing the code (in Matlab 2020b) for the semi-automatic processing of the CO 2 -CIBR and HDFI. The hypercube was visually explored to identify the noisy bands affecting the spectra; specifically, the bands within two VNIR spectral intervals (402.2041-426.7687 nm and 972.3630-959.4313 nm) and two SWIR intervals (942.8875-969.7985 nm and 2456.2781-2496.8254 nm) were removed.…”

Section: Prisma Scenementioning

confidence: 99%

Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

Amici

Piscini

2021

Remote Sensing

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Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by the ASI (Agenzia Spaziale Italiana, Italian Space Agency) mission launched in 2019 to measure the unique spectral features of diverse materials including vegetation and forest disturbances. In this study, we explored the potential use of this new sensor PRISMA for active wildfire characterization. We used the PRISMA hypercube acquired during the Australian bushfires of 2019 in New South Wales to test three detection techniques that take advantage of the unique spectral features of biomass burning in the spectral range measured by PRISMA. The three methods—the CO2-CIBR (continuum interpolated band ratio), HFDI (hyperspectral fire detection index) and AKBD (advanced K band difference)—were adapted to the PRISMA sensor’s characteristics and evaluated in terms of performance. Classification techniques based on machine learning algorithms (support vector machine, SVM) were used in combination with the visual interpretation of a panchromatic sharpened PRISMA image for validation. Preliminary analysis showed a good overall performance of the instrument in terms of radiance. We observed that the presence of the striping effect in the data can influence the performance of the indices. Both the CIBR and HFDI adapted for PRISMA were able to produce a detection rate spanning between 0.13561 and 0.81598 for CO2-CIBR and that between 0.36171 and 0.88431 depending on the chosen band combination. The potassium emission index turned out to be inadequate for locating flaming in our data, possibly due to multiple factors such as striping noise and the spectral resolution (12 nm) of the PRISMA band centered at the potassium emission.

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Prisma Hyperspectral Mission Products

Cited by 31 publications

References 1 publication

The potential of spectral unmixing method applied to PRISMA hyperspectral images in the identification of Li minerals: an evaluation for prospecting purposes

The potential of spectral unmixing method applied to PRISMA hyperspectral images in the identification of Li minerals: an evaluation for prospecting purposes

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

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