Edge detection in Cassini astronomy image using Extreme Learning Machine

Abstract

Edge detection is often performed on disc-like object in Cassini astronomy images to accurately obtain the object's center position. The existing edge extraction methods usually produce lots of false edge pixels because of noise and the interior details in disc-like objects. In the paper, an edge detection algorithm based on Extreme Learning Machine (ELM) is proposed for Cassini astronomy images. In the ELM model, a 28-D feature vector of a pixel in Cassini image is constructed as input, which consists of first and second derivatives and some Haar-like features, and a binary classifier is obtained as output that tells if the pixel is in edge. The experimental result shows that its performance is much better than traditional operators. The detected edge is closer to the actual contour. Its average accuracy is 0.9379. The algorithm can be applied to edge detection of disc-like objects in astronomy images.