Coffee plantation area recognition in satellite images using Fourier transform
2017
Tsai, Du-Ming | Chen, Wan-Ling
In this study, a machine vision scheme is proposed for coffee plantation area recognition in satellite images. It automatically segments the row-planted coffee field from forest trees and irrelevant areas in the image. The result can be used for coffee yield estimation to improve the supply and demand of coffee commodity in the market. Commercial coffee plantation grows coffee trees in rows along a specific direction to increase the production yield and management efficiency. The coffee plants and forest trees present the same color tone in the image and, thus, color cannot be used for the discrimination. The row-planting pattern of coffee trees shows structural texture in the satellite image. This study presents a Fourier transform-based method to extract structural features in the spectral domain for image segmentation. Row-planted coffee fields generate high-energy frequency components in a single direction, while naturally-growing plants present omnidirectional frequency components in the spectral domain image. The main frequency in the power spectrum indicates the number of parallel lines in a small patch window and, thus, gives the density feature. The density feature for the row-planted coffee filed is equivalent to the number of rows in a unit square area, whereas it is only one for the randomly-growing plants. This study analyzes the satellite images of coffee plantation regions in different times with varying illuminations and growing stages in Brazil, Africa, Vietnam and Hawaii. The experimental results have shown that the Fourier-based structural and density features can provide correct segmentation to distinguish the row-planted coffee field from irrelevant vegetation areas in the satellite image.
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