Study Feature-level Information Fusion Based On BP Network And Its Application In Target Recognition

Automatic target recognition and useful information extraction is a key application of remote sensing data. Though more than two decades has been past since the utilization of multispectral remote sensing images for object recognition and extraction, the accuracy as well as efficiency of target recognition is still far from satisfaction, partially due to the fact that relatively simple target features are used in the existing approaches. This paper expounds a methodology which employs spectral reflectance, geometric property and texture feature of a given target synergistically under the framework of feature-level information fusion. The results show the accuracy of recognition is increased in general and the efficiency improved in particular.Information fusion technology is a newly arisen technology of comprehensive processing of multiple source information. In the field of image processing of remote sensing, pixel-level fusion technology is very ripe. When studing object recognition and extraction from remote sensing images, we realize the importance of feature-level information fusion more and more clearly. For spectrum feature, geometry shape feature and texture feature are irrelevant characteristic vector each other, it is difficult to integrate these vectors into a criteria vector. Neural network doesn't have the limitation of data type and distribution function. More over, it has more flexible requisitions for data and more high tolerant degree. According to these merits, this paper puts forward the feature-level information fusion system based on BP Network, besides studies its application in target recognition, and regards airport from the remote sensing images as the goal of target recognition.The detailed research work can be sumed up as the following:At first, the thesis collects and summarizes the latest research results and progress of information fusion technology, especially feature-level fus on technology and its advantages and disadvantages. Secondly, the theory of the artificial neural network (ANN) technology is introduced. This thesis regards BP network as the main research object, and discussed the designs of BP network structure and parameter in detail. In succession, a list of classical feature chosen and extraction methods is explained, and the experiments of those methods that facing to spectrum feature, geometry shape feature and geometry texture feature are given. Then feature-level information fusion technology based on BP network are discussed in detail, and brings forth target recognition system based on the technology, which integrated the multi-source characteristic of information fusion and strong non-linear processing ability of neural network. That system has strong feasibility and efficiency proved by airport recognition and extraction from middle resolution remote sensing images. In the end, this thesis reviews the work that had been done by the author in theory and application, pointes out the research direction and improvement direction in the future.