Title :
Crater Detection Based on Gist Features
Author :
Jihao Yin ; Hui Li ; Xiuping Jia
Author_Institution :
Sch. of Astronaut., Beihang Univ., Beijing, China
Abstract :
Craters are the most abundant landform on the planet surface, which could provide fundamental clues for planetary science. Due to variations in the terrain, illumination, and scale, it is challenging to detect craters through remote sensing images and it requires an effective crater feature extraction method. In this paper, we address this problem using Gist features, which can provide highly effective descriptions on crater´s local edges and global structure. The proposed crater detection procedure contains three key steps. First, we extract all candidate craters on a planet image using a boundary-based technique. Second, Gist features are generated from selected training samples. Third, crater detection is conducted using Gist feature vectors with random forest classification. Compared to pixel-based and Haar-like features, our method shows more accurate crater recognition, and achieves satisfied results in the experiments conducted on the Mars Orbiter Camera (MOC) database.
Keywords :
astronomical image processing; feature extraction; object recognition; planetary remote sensing; planetary surfaces; random processes; Gist feature vectors; Mars Orbiter Camera database; boundary-based technique; crater detection; crater feature extraction method; crater recognition; planet surface; random forest classification; Feature extraction; Image edge detection; Mars; Remote sensing; Shape; Vectors; Crater detection; gist features; random forest;
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
DOI :
10.1109/JSTARS.2014.2375066