Detecting ship targets distributed in infrared images of clouds, waves and other complex disturbances in an unknown complex background, and determining the true ship target in the case where the target and the false alarm are very similar are widely used and challenging tasks. This paper proposes an infrared ship targets detection algorithm based on Bayesian theory and SVM combination. Bayesian theory can be used to estimate the probability of partial unknowns with incomplete information. Bayesian formula can be used to correct the probability of occurrence, and finally we can use the expected value and the modified probability to make the optimal decision. At the same time, support vector machine (SVM) is a novel small sample learning method with solid theoretical foundation. It does not involve probability measures and laws of large numbers, so it is different from existing statistical methods. Firstly, the paper introduces the infrared ship target detection method based on Bayesian theory. Then, the image is post-processed to remove redundant false alarm targets. Finally, the paper introduces the experimental data and the performance evaluation indicators of ship detection results, and compares with other ship detection methods to obtain experimental results.
Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by Kmeans clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.