Advanced Color Edge Detection Using Clifford Algebra in Satellite Images

Edge detection is widely used for image processing to improve the detection and classification of objects, segmentation, and extraction of other features. Satellite images are rich in information about objects with different color intensity and have a large amount of noise, so it is difficult to achieve recognition, classification, and feature extraction of small objects through traditional edge detection algorithms. The colors in satellite images suffer from a large amount of overlap due to areas or weather conditions that generate a lot of noise. Edge detection provides detailed information about objects in an image by reducing unnecessary feature information. Edge detection in color images is more challenging than edge detection in gray-level images. This paper proposes a method for the edge detection of color images using Clifford algebra and its sub-algebra, quaternions. Quaternion-based Fourier transform is used to process red, green and blue (RGB) images separately in the vector field. A 3×3 quaternion mask is developed to filter out frequencies of the image in multiple directions and only provides details about the edges. The algorithm works on three channels individually; the output is then processed through quaternion Fourier transform (QFT) and inverse QFT with a 3×3 mask to filter high frequencies. The proposed algorithm is compared with traditional edge detection algorithms using a satellite image dataset that has different types of objects and detailed information. Results are validated through entropy, structure similarity, and noise error to prove that our proposed algorithm provides satisfactory performance on different remote sensed images.