Research on image feature point extraction methods of low altitude remote sensing

Abstract

Nowadays UAVs are increasingly used to collect low altitude remote sensing images. But because of the instability of such flight platforms, collected images usually show some obvious disadvantages, such as the large scale difference, the large swing angle, with the result that the traditional matching methods are difficult to obtain satisfactory results of extracted feature points. That causes an immense obstacle in the feature matching between adjacent images. However, advantages of low altitude remote sensing are more appealing, which can provide very high spatial resolution images with more features and it is very useful especially in small area environmental monitoring. In fact, the gray-level-based operator of point-feature-based image matching method has been widely used in recent years for its rapidity, accuracy and stronger ability to resist deformation. In this paper, through contrast experiments, the authors compare the extracting performance among the three kinds of gray-level-based operators (the Forstner operator, the Harris operator and the SIFT operator) and find that the SIFT operator has a stable performance and the best property under various conditions which is nice to satisfy the functional requirement of the feature point extraction, with a strong practicability in the field of low altitude remote sensing image preprocessing. Besides, this paper puts forward a new Gauss Pyramid simplified model and descriptor generation method on the theory level and shows that the stability and timeless of the improved SIFT are better than the traditional algorithm through comparative experiment.