River Detection Using Fast Fourier Transform, Contour Detection, and Hough Line Transform

River environment presents significant challenges compared to the marine environment due to its diverse and complex features. Processing data from rivers, particularly image data, is inherently difficult because of this complexity. This paper presents a method for detecting river areas in images using a Fast Fourier Transform (FFT), contour detection, and Hough line transform. The method leverages the characteristic of water surfaces having a uniform color, which results in distinct low-frequency components in the frequency domain. By this characteristic, the proposed system effectively identifies river regions with high accuracy. The methodology encompasses several crucial steps: image pre-processing, application of the Fast Fourier Transform (FFT) to analyze frequency components, thresholding to isolate significant frequencies, noise reduction through contour selection, and line detection using the Hough Line Transform. The proposed method has been tested on a diverse dataset of river images, demonstrating an impressive accuracy of up to 98%. This approach offers a robust solution for river detection, effectively addressing the challenges posed by varying river environments.