Principle Component Analysis for Crop Discrimination using Hyperspectral Remote Sensing Data
Pooja Vinod Janse1, Ratnadeep R. Deshmukh2
1Miss. Pooja V. Janse*, Department of Computer Science and Engineering and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (Maharashtra), India.
2Dr. R. R. Deshmukh, Professor, Department of Computer Science and Engineering and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (Maharashtra), India.
Manuscript received on June 20, 2021. | Revised Manuscript received on June 30, 2021. | Manuscript published on July 30, 2021. | PP: 40-43 | Volume-10, Issue-9, July 2021 | Retrieval Number: 100.1/ijitee.I92970710921 | DOI: 10.35940/ijitee.I9297.0710921
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Crop Discrimination, ASD FieldSpec 4 Spectroradiometer, Principle Component Analysis (PCA), vegetation indices.