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Principle Component Analysis for Crop Discrimination using Hyperspectral Remote Sensing Data

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 30 Jul 2021Publisher:Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESPJournal:International Journal of Innovative Technology and Exploring Engineering, volume 10, pages 40-43 (eissn: 2278-3075,

Authors: Pooja Vinod Janse; Ratnadeep R. Deshmukh;

doi: 10.35940/ijitee.i9297.0710921

Principle Component Analysis for Crop Discrimination using Hyperspectral Remote Sensing Data

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Abstract

Crop discrimination is still very challenging issue for researcher because of spectral reflectance similarity captured in non-imaging data. The objective of this research work is to focus on crop discrimination challenge. We have used ASD FieldSpec4 Spectroradiometer for collection of leaf samples of four crops Wheat, Jowar, Bajara and Maize. We used vegetation indices and some spectral reflectance band for featuring our dataset. We applied Principle Component Analysis (PCA) for discrimination and it has been observed that when we use first and second principle component, it will give poor result but if third principle component is used then we get accurate and fine results.

Related Organizations

Dr. Babasaheb Ambedkar Marathwada University
India

Keywords

Crop Discrimination, ASD FieldSpec 4 Spectroradiometer, Principle Component Analysis (PCA), vegetation indices.

1 Research products, page 1 of 1

Crop Discrimination using Non-Imaging Hyperspectral Data
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