In order to detect satellite under sky background, we propose an optimized satellite object detection extraction and tracking algorithm under the sky background. The proposed satellite tracking processing consists of two stages. In the first stage of object detection and extraction, the background template based on the mixture Gaussian model is used to establish background frame, and then the background is removed by inter-frame difference method to obtain the object. In the subsequent object tracking stage, this paper proposes an improved untracked Kalman filter algorithm for object tracking. Firstly, it tracks multiple suspected objects in the background, and then introduces a path coherence function to eliminate the false objects. Compared with other methods, the experimental results show that our method can better meet the real-time requirement, eliminate false objects appeared in the sequence of images more efficiently and make the tracking trajectory smoother.
The orithogonal subspace projection (OSP) method needs all the endmember spectral information of observation area
which is usually unavailable in actual situation. In order to extend the application of OSP method, this paper proposes an
algorithm without any priori information based on OSP. Firstly, the background endmember spectral matrix is obtained by
using unsupervised method. Then, the OSP projection operator is calculated with the background endmember matrix.
Finally, the detection operator is constructed by using the projection operator, which is used to detect the hyperspectral
imagery pixel by pixel. In order to increase the detection rate, local processing is proposed for anomaly detection with no
prior knowledge. The algorithm is tested with AVIRIS hyperspectral data, and binary image of targets and ROC curves are
given in the paper. Experimental results show that the proposed anomaly detection method based on OSP is more effective
than the classic RX detection algorithm under the case of insufficient prior knowledge, and the detection rate is
significantly increased by using the local processing.
Infrared and visual image registration has a wide application in the fields of remote sensing and military. Mutual
information (MI) has proved effective and successful in infrared and visual image registration process. To find the most
appropriate registration parameters, optimal algorithms, such as Particle Swarm Optimization (PSO) algorithm or Powell
search method, are often used. The PSO algorithm has strong global search ability and search speed is fast at the beginning,
while the weakness is low search performance in late search stage. In image registration process, it often takes a lot of time to do useless search and solution’s precision is low. Powell search method has strong local search ability. However, the search performance and time is more sensitive to initial values. In image registration, it is often obstructed by local
maximum and gets wrong results. In this paper, a novel hybrid algorithm, which combined PSO algorithm and Powell search method, is proposed. It combines both advantages that avoiding obstruction caused by local maximum and having higher precision. Firstly, using PSO algorithm gets a registration parameter which is close to global minimum. Based on the result in last stage, the Powell search method is used to find more precision registration parameter. The experimental result shows that the algorithm can effectively correct the scale, rotation and translation additional optimal algorithm. It can be a
good solution to register infrared difference of two images and has a greater performance on time and precision than
traditional and visible images.