Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Crop classification

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    Temporal crop monitoring with sentinel-1 sar data
    (Springer Science and Business Media Deutschland GmbH, 2021) Salma, S.; Dodamani, B.M.
    Spatial and temporal analysis of crops and other land surface features is the major application of the present spaceborne sensors. Among most of the spaceborne sensors, synthetic aperture radar (SAR) is having the advantage of all-weather capability with low-frequency bands. SAR data is useful for decompositions, crop classifications, etc. In this study, paddy fields are classified using Sentinel-1 ground range detection. Synthetic aperture radar data with the combination of vertical polarization with the horizontal receiver (VV and VH) is selected for the temporal variation analysis and classification analysis of paddy fields along with the plantations. Multi-temporal classification analysis is done using random forest classifier, and correlation obtained is 0.78 and 0.45 in VH and VV polarization, respectively, and the error rate shows significant variation in both the polarizations, i.e., 0.05 and 0.25 (in VH and VV polarizations, respectively), which indicates more error rate in VV polarization band. In this study area, VH polarization shows better classification and correlation compared to VV polarization due to double bounce effect of urban features, paddy and plantation at the stem elongation and booting stage in VV polarization. © 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.