Onion mapping using PRISMA hyperspectral image: comparative analysis of K-means classification and linear spectral unmixing

Reymar R. Diwa; Keith Ann E. Cabello; Ariel C. Blanco

doi:10.1117/12.3041512

20 December 2024 Onion mapping using PRISMA hyperspectral image: comparative analysis of K-means classification and linear spectral unmixing

Reymar R. Diwa, Keith Ann E. Cabello, Ariel C. Blanco

Author Affiliations +

Proceedings Volume 13266, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII; 132660P (2024) https://doi.org/10.1117/12.3041512
Event: Asia-Pacific Remote Sensing, 2024, Kaohsiung, Taiwan

Conference Poster

Abstract

Onion is a high-value crop that is highly susceptible to price fluctuations in the Philippines due to weather conditions, global political situations, and other factors. Accurate mapping and yield monitoring is crucial for managing these fluctuations and ensuring stable supply chains. Unlike multispectral satellite images, hyperspectral images offer higher spectral resolution that enable it to differentiate subtle variations in the spectral signatures of onions compared to other crops. Thus, this study explores the effectiveness of PRISMA hyperspectral imagery for mapping onion fields through two distinct methodologies: K-means unsupervised classification and Linear Spectral Unmixing (LSU). The PRISMA image, captured on February 4, 2024, covers the area of Bongabon, Nueva Ecija, known as the onion capital of the Philippines, and its surrounding municipalities. The Level 2D product was denoised using Minimum Noise Fraction (MNF) by Forward MNF followed by Inverse MNF. The dimensionality of the image was then reduced using Principal Component Analysis (PCA). Three sets of data inputs - PC 1-2, PC 1-4, and 175 PRISMA bands - were classified using K-means. Separately, linear spectral unmixing was performed using four representative spectral signatures for each class - onion, rice, and soil – extracted from denoised PRISMA using known field locations. By comparing the outcomes of these methodologies, this research evaluates their accuracies in delineating the onions, with LSU providing more precise quantification of onion extent. The results highlight the potential of hyperspectral remote sensing in precision farming and in effective mapping and monitoring of onion yields to help mitigate market volatility.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Reymar R. Diwa, Keith Ann E. Cabello, and Ariel C. Blanco "Onion mapping using PRISMA hyperspectral image: comparative analysis of K-means classification and linear spectral unmixing", Proc. SPIE 13266, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII, 132660P (20 December 2024); https://doi.org/10.1117/12.3041512

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