Fourier analysis for detecting vegetation in hyperspectral images

Introduction: hyperspectral images, unlike conventional images, are composed of numerous channels that provide detailed information about the spectral signatures of objects. This allows for the identification of the materials that make them up, and given their potential for detecting environmental changes, identifying vegetation in urban settings using effective computational methods becomes relevant. Objective: the objective of this research is to propose a computational method based on Fourier analysis for detecting vegetation in hyperspectral images. Methods: the research was developed in four methodological phases: selection of technologies, acquisition of the characteristic vegetation pixel, determination of phase similarity between the characteristic pixel and vegetation and non-vegetation pixels, validation of the method on a test hyperspectral image. A method was implemented using the spectral and numpy libraries in Python. Results: the Fourier analysis yielded an average phase similarity of 89.89% and a minimum similarity of 64.54% between the characteristic vegetation pixel and 100 training vegetation pixels. For non-vegetation pixels, the average phase similarity was 42.19%, with a maximum similarity of 63.98%. These results indicate that the proposed method successfully differentiates between vegetation and non-vegetation pixels. Conclusion: the results demonstrate that the Fourier-based method can accurately identify vegetation areas in hyperspectral images, showing non-overlapping phase similarities between vegetation and non-vegetation. This validates the effectiveness of the proposed approach in detecting vegetation in urban environments.