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.
Highlights
The high-quality spectral information in a hyperspectral image can better classify and identify the minerals in a mineral-rich area
1.1 Principal Component Analysis In Digital image processing techniques, PCA is mathematically defined as an orthogonal linear transformation that transforms the hyperspectral image data to a new coordinate system in which the greatest variance by some projection or rotation of the data comes to lie on the first coordinate, the second greatest variance on the second coordinate, and so on (Harsanyi and Chang, 1994)
On comparing with lithological Map, the results of various data dimensionality reduction or feature extraction techniques show some interesting results for the geology of the study area
Summary
The high-quality spectral information in a hyperspectral image can better classify and identify the minerals in a mineral-rich area. Launched PRISMA and DESIS datasets with good spectral quality in bandwidth and SNR can prove to be a boon for geoscientists in mapping the remote and geologically rich areas on Earth's surface. These datasets will give good results after applying feature extraction techniques thanks to comparative better spectral quality. This work focuses on applying linear data dimensionality reduction techniques to newly launched hyperspectral datasets: PRISMA and DESIS. Later few narrowband ratios developed in the VNIR (Visible to Near-Infrared) and SWIR (Short wave Infrared) range using PRISMA hyperspectral datasets to check the mineral abundance (Kalinowski and Oliver, 2004). These narrowband ratios are developed initially for ASTER datasets, but they have been transformed to narrowband ratios suitable for PRISMA hyperspectral datasets in this work
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