An Efficient and Precise Remote Sensing Optical Image Matching Technique Using Binary-Based Feature Points

Reflection-Aware Generation and Identification of Square Marker Dictionaries

Square markers are a widespread tool to find correspondences for camera localization because of their robustness, accuracy, and detection speed. Their identification is usually based on a binary encoding that accounts for the different rotations of the marker; however, most systems do not consider the possibility of observing reflected markers. This case is possible in environments containing mirrors or reflective surfaces, and its lack of consideration is a source of detection errors, which is contrary to the robustness expected from square markers. This is the first work in the literature that focuses on reflection-aware square marker dictionaries. We present the derivation of the inter-marker distance of a reflection-aware dictionary and propose new algorithms for generating and identifying such dictionaries. Additionally, part of the proposed method can be used to optimize preexisting dictionaries to take reflection into account. The experimentation carried out demonstrates how our proposal greatly outperforms the most popular predefined dictionaries in terms of inter-marker distance and how the optimization process significantly improves them.

Real-time and high precision feature matching between blur aerial images

When aerial cameras get aerial remote sensing images, the defocus will occur because of reasons such as air pressure, temperature and ground elevation changes, resulting in different image sharpness of continual aerial remote sensing images. Nowadays, the rapidly developing feature matching algorithm will rapidly reduce the registration rate between images with different image sharpness. Therefore, in order to enable aerial cameras to get image sharpness parameters according to the locations of aerial image feature points with inconsistent sharpness, this paper proposes a feature matching algorithm between aerial images with different sharpness by using DEM data and multiple constraints. In this paper, the feature matching range is extended according to the modified aerial imaging model and the nonlinear soft margin support vector machine. Then the relative moving speed and its variation of the feature points in the image are obtained by using the extended L-k optical flow, and finally the epipolar geometric constraint is introduced. To locate the feature points is obtained under multiple constraints, there is no need to calculate the feature point descriptors, and some mismatched point pairs are corrected, which improves the matching efficiency and precision. The experimental results show the feature matching precision of this algorithm is more than 90%, and the running time and matching precision can meet various application needs of aerial cameras.