Conference Papers

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    High-resolution mapping of soil properties using aviris-ng hyperspectral remote sensing data—a case study over lateritic soils in mangalore, india
    (Springer Science and Business Media Deutschland GmbH, 2021) Chitale, M.M.; Kundapura, S.
    Quick and accurate mapping of properties of soil is considered to be critical for agriculture and environmental management. Rapid assessment of soil properties is a daunting task in monitoring the environment. The conventional field sampling is a laborious as well as time-consuming job. The conventional methods are restricted to a specific region but there is a need to analyses the soil properties at landscape levels. Hence, this study emphasises on hyperspectral remote sensing which to some extent helps in rapid assessment of the properties. The hyperspectral data used for the study is AVIRIS-NG data. The study explored the potential of AVIRIS-NG hyperspectral data in mapping soil properties which were analysed by in situ laboratory methods and compared with them by geostatistical method of spatial interpolation. Hence, the method adopted for this purpose is the study on spatial variability of soil properties by using Kriging interpolation technique. Also, a review study is carried out on the visible and near-infrared analysis (VNIRA), multiple regression analysis approach and spectral angle mapper supervised classification technique on the high-resolution AVIRIS-NG Hyperspectral data, which will yield as an empirical model for predicting the soil property in question from both wet chemistry and spectral information of a representative set of samples and classifies the data accordingly. © Springer Nature Singapore Pte Ltd 2021.
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    Crop Classification Based on Optimal Hyperspectral Narrow Bands Using Machine Learning and Hyperion Data
    (Institute of Electrical and Electronics Engineers Inc., 2023) Reddy, B.S.; Sharma, S.; Shwetha, H.R.
    In view of global climate change and the limited availability of cropland, crop classification plays a critical role in maintaining food security. Hyperspectral remote sensing has emerged as a valuable tool for classifying crops using detailed spectral information. To explore the potential of hyperspectral data for nationwide crop classification, the research uses the GHISACONUS library to identify Optimal Hyperspectral Narrow Bands (OHNBs) across seven Agricultural Experimental Zones (AEZ) in the USA. Principal Component Analysis (PCA) techniques are employed to identify 24 OHNBs from the data. OHNBs achieved notable accuracy rates, ranging from 75% to 91% when classifying different crop types and their growth stages. However, accuracy drops below 90% in significant cases, likely due to the limited selection of 24 OHNBs and the variation in crop phenology across the seven study areas. The research indicates that systematically selecting OHNBs based on crop phenological stages consistently achieves satisfactory classification accuracy. This approach effectively classifies crops in any Hyperion image. Overall, the study contributes significantly to our knowledge of using OHNBs for nationwide crop classification, highlighting the importance of considering phenological stages and data acquisition conditions to enhance accuracy. © 2023 IEEE.