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In Cassini ISS (Imaging Science Subsystem) images, contour detection is oftenperformed on disk-resolved object to accurately locate their center. Thus, thecontour detection is a key problem. Traditional edge detection methods, such asCanny and Roberts, often extract the contour with too much interior details andnoise. Although the deep convolutional neural network has been appliedsuccessfully in many image tasks, such as classification and object detection,it needs more time and computer resources. In the paper, a contour detectionalgorithm based on H-ELM (Hierarchical Extreme Learning Machine) and DenseCRF(Dense Conditional Random Field) is proposed for Cassini ISS images. Theexperimental results show that this algorithm's performance is better than bothtraditional machine learning methods such as SVM, ELM and even deepconvolutional neural network. And the extracted contour is closer to the actualcontour. Moreover, it can be trained and tested quickly on the generalconfiguration of PC, so can be applied to contour detection for Cassini ISSimages.

Contour Detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field