Research On Detection And Recognition For Infrared Imaging Target

The utilization of infrared imaging in automatic target detection and recognition is one of the main technological development directions of modern military weapon equipments and a key technique for military weapon systems, and has been the subject of intense investigation in recent years. The intensified battlefield rivalry under modern high technique conditions requires weapon systems to be capable of detecting and recognizing various targets in complex natural background and artificial interference.Sponsored by the research project of automatic target detection and recognition in infrared imaging guidance, this thesis conducts a thorough study on image preprocessing, target detection and recognition in light of specific application requirements. The main research results are as following:1. On the basis of investigation of infrared image preprocessing (such as noise smooth and cluster suppression), jitter compensation and registration of dual-mode image, multimodality image fusion based on wavelet multi-resolution analysis and multimodality image fusion based on Contourlet multi-scale geometry analysis against the problem of fusion of visual and infrared images, this paper presents a novel fusion detection algorithm based on energy and region correlation and analyses the performance evaluation criteria of image fusion algorithm.2. Directed against the problem of lower detection probability of small IR targets in complex backgrounds, an improved M-estimation filtering algorithm for suppressing background clutters based on residual improvement is proposed. This algorithm introduces a basic model of M-estimation to predict background, and treats target pixels and observed noises as the mixed interference of background estimation. It uses the correction function related to residual to adaptively adjust gain to reduce influence of abnormal samples on background estimation so as to increase the accuracy of estimation. Meanwhile, the proposed algorithm introduces a forget factor to make the algorithm adaptive to non-homogeneous background prediction to improve the robustness of the algorithm.3. Directed against the problem of lower detection probability of traditional pipeline filter algorithm due to marginal noise interference on pipeline center coordinates, a variable weighted pipeline filter algorithm is presented for detecting small targets in IR image sequences. An adaptive learning scheme is employed to modify pipeline center coordinates in real time according to targets'positions. This method can effectively restrain marginal noise interference. Experiments show that detection performance is significantly increased by using the proposed algorithm.4. Segmentation of infrared images based on mean shift is investigated. Directed against over-segmentation of the mean shift based segmentation algorithm under the condition of inhomogeneous gray distribution of objects and complex background, an infrared image segmentation approach based on mean shift and normalized cut is proposed. This approach segments an image from coarse to fine and can effectively eliminate over-segmentation so as to ensure accurateness of segmentation.5. Directed against the problem that the selection of the initial values for Newton iteration in the Fast ICA algorithm is very sensitive and of the disordered extraction of independent elements, the author proposes a rapid independent element analysis algorithm based on distance function criteria. A one dimension search strategy is imposed on the direction of Newton iterative to ensure convergence of the algorithm robust to initialization. Meanwhile, new feature selection criteria based on distance function are established to select optimal features favorable for object recognition according to the characteristics of infrared image. It overcomes the shortcoming that infrared object recognition rate and stability decrease with the increasing of training image samples, which set up the subspace dimension of characteristics also causes a corresponding increase. Experimental results show that the improved Fast ICA algorithm has lower error classification rate which is robust to different kinds of classes. A new infrared multi-object classification algorithm based on combination of Hadamard Error Correcting Output Code and K-NN is proposed. Experimental results show that this algorithm has the distinguishing feature of anti-jamming and increases accurateness of classification.