Abstract
•We present a performance comparison of state of the art feature point detector and descriptor algorithms.•Comparison is carried out on aerial and satellite images of agriculture land.•Objective is to identify well deserving feature points for the images of agriculture land.•The agriculture land images possess high textural, photometric, and temporal differences.•We also propose a new descriptor MN-SIFT, which outperforms all other descriptors on the images of agriculture land.
This paper focuses on image matching between satellite imagery and aerial photographs of agriculture land. Feature points are used for image matching. The satellite imagery and aerial photographs were acquired at different times, viewpoints, sensors, and altitudes. Therefore, they possess very high temporal, photometric, and projective differences. When feature points, such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) are applied on such images, they demonstrate poor performance. This paper aims at evaluating the performance of SIFT, SURF, and other state of the art feature points in order to determine well deserving feature points for the images of agriculture land. We also propose a new feature point descriptor, i.e. Modified Normalized Gradient SIFT, which obtains on average 1.73–2.37% better performance than other state of the art descriptors.