Feature Extraction of Desis and Prisma Hyperspectral Remote Sensing Datasets for Geological Applications

Abstract

Abstract. With the recent launch of advanced hyperspectral satellites with global coverage, including DESIS and PRISMA, a massive volume of high spectral resolution data is available. This work is focused on the spectral analysis and implementation of feature extraction or data dimensionality reduction techniques on both newly available datasets for geological interpretation. Three of the best feature extraction algorithms, Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA), were tested for lithological mapping for the Rajasthan state of India. The present work demonstrates the advantage of the feature extraction algorithm in geological mapping and interpretability as it shows the excellent performance for these datasets. The narrowband ratios for the clay minerals, dolomite, kaolinite, amphiboles, and Al-OH are generated using the PCA and MNF components. All of these band ratios were compared with the Lithological Map available. It is concluded that PCA is the first choice for feature-based lithological classification of hyperspectral remote sensing data. ICA is giving some impressive results which can be explored further. DESIS and PRISMA have a 30 km swath, finer spectral resolution, and high signal-to-noise ratio, which shows much potential in lithological mapping over the parts of northern India. It is suggested to use advanced feature extraction algorithms with recently launched hyperspectral data for accurate and updated mineral mapping over a sizeable geological importance area.

PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

“…Accordingly, alteration mineral maps derived from PRISMA are specifically inclusive compared to previous studies using ASTER multispectral data in the study region [71][72][73][74]. In addition, some previous studies accentuated that PRISMA datasets contain great capability providing valuable insights for hydrothermal alteration mapping of ore mineralizations in metallogenic provinces around the world [2,[7][8][9][14][15][16][17][18][19][20][21][22][23][24][25]75,76]. The escalating challenges associated with mineral exploration underscore the critical need for innovative strategies, particularly in the identification of high-potential zones and subsequent campaigns.…”

“…It enables the capture of images with a superior signal-to-noise ratio (SNR) compared to the Hyperion sensor. Essentially, it serves as a satellite-based earth observation mission designed to provide spectroscopic imagery, fostering the development of innovative methods and applications for the management and analysis of ore mineral resources [2,[7][8][9][10]. The coexistence of minerals with similar spectral properties often leads to spectral mixing, resulting in overlapping absorption regions and highly correlated spectra [11,12].…”

“…These challenges, including diverse spectral features and limited ground samples, hinder the function of image processing algorithms and remote sensing sensors. Addressing these issues, the use of PRISMA hyperspectral imagery in mineral exploration across various geographic and geological settings highlights the adaptability and effectiveness of PRISMA imagery in overcoming challenges posed by spectral mixing, light scattering effects, and intricate spectral patterns [2,[7][8][9][10].…”

“…The Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), Independent Component Analysis (ICA), Adaptive Coherence Estimator (ACE), Random Forest (RF), XGboost (XGB), Support Vector Machine (SVM) and many other machine-learningbased classification algorithms were used for processing PRISMA data in mapping alteration minerals, identifying economic mineralization prospects, delineating dolomitization, obtaining high-quality reflectance estimations and achieving high-accuracy lithological mapping [2,[7][8][9][10][14][15][16][17][18][19]. Additionally, novel approaches and algorithms such as the spectral hourglass, iterative informed spectral unmixing technique, fuzzy logic approach, GIS-based algorithm, and informed linear mixing model were implemented to PRISMA data to determine the potential of the dataset for automated alteration mineral identification in diverse environments [20][21][22][23][24][25].…”

Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support

“…The literature on the use of PRISMA data, particularly in the field of land cover classification, is still limited. Most of the research shows the potential of PRISMA data for specific purposes, such as forest conservation with wildfire fuel mapping [28] or fire detection [29], geological applications [30], cryospheric applications [31], urban surface detection [32], and mapping methane point emissions [33]. There are also interesting studies in the agricultural field dealing with specific crop or vegetation type discrimination [34][35][36][37].…”