The Research On Methods And Application Of Multi-source Remote Sensing Imagery Fusion

The progress on remote sensing technology enriches available data. Data acquiredby various types of sensors are different. The technology of multi-source remotesensing data fusion is an effective means for the comprehensive utilization of variousdata.At present, mostly remote sensing imagery fusion research concentrates on thefusion methods of pixel level. Research on feature level and decision level isrelatively little. This paper proposes new fusion methods on feature level and decisionlevel, which are applied in the research of urban "heat island effect" and landscapeanalysis. The main work is as follows.(I) Multi-feature fusion method is studied. Serial fusion is a common used methodfor feature fusion, which is a combination in geometric meaning and can noteffectively use the correlation between features. In this paper, two algorithms aredescribed to fuse multi-feature. The multi-view spectral embedding is introducedto remote sensing imagery fusion for the first time. Multi-view spectral embeddingtheory is based on the principle of matrix equivalent to transform data fromdifferent feature spaces into the same representation space. Moreover, thepaper presents a novel fusion algorithm based on feature proximity. Attraction isdefined to represent the relationship of feature vector and prototype models. Theproximity vector formed by attraction between prototype models and feature vector isadopted to represent fused feature. The two algorithms are used in multi-spectral andhyper-spectral remote sensing image classification. Experimental results show thatthe two fusion algorithms classify the samples at the correct rate of over90%and they reduce the dimension of feature space significantly.(II) Decision fusion method is studied. To solve some problems in dynamicintegration of multiple classifiers such as high time complexity, additional dynamicmechanisms, instability of dynamic integration, a decision fusion algorithm based on judgment matrix is proposed. Inspired by the behavior knowledge space fusion, theselection of classifier is based on priori knowledge of the training samples. A trainingsample is inputted into the basic classifiers, and its corresponding type is judged bythe basic classifiers. Record the classification results and the classifier numbers thatcan correctly identify the input type in judgment matrix. In the process of dataclassification, the judgment matrix is accessed according to the output of basicclassifiers to find the reliable classifiers. The type of test sample is obtained inaccordance with the majority. The dynamic selection mechanism is recorded in thematrix so the problem of setting artificial parameters does not exist. It is easy toacquire the integration results. The fusion algorithms based on decision matrix andbehavior knowledge space are performed on the same basic classifiers. Experimentalresults show that the fusion algorithm based on decision matrix can achieve betterclassification results.(III) The proposed methods are used to analysis the "heat island effect" andlandscape pattern of Qingdao. In the research of "heat island effect", the groundtemperature in densely populated area is6degrees warmer than that in mountainousarea of Laoshan. The results show that the aggregation of human changes thesurrounding environment and energy release in the process of human activitiesaggravates the “heat island effect”. The landscape pattern indexes show thatresidential area patches are fragmentized, which makes material circulationconvenient. The green lands are scattered in residential areas so their ability to relieve"heat island effect" is limited. There are many forest patches and a long coastline,which makes Qingdao own nice natural conditions. The urbanization has a significanteffect on environment. Therefore, the future development plan should pay moreattention to environmental protection and enhance the city livable index.