tailieunhanh - Geoscience and Remote Sensing, New Achievements Part 4

Tham khảo tài liệu 'geoscience and remote sensing, new achievements part 4', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 98 Geoscience and Remote Sensing New Achievements In these articles we find two facts that we try to avoid On one hand the lack of generalization by using a predefined lexicon when trying to link data with semantic classes. The use of a semantic lexicon is useful when we arrange an a priori and limited knowledge and on the other hand the need of experts in the application domain to manually label the regions of interest. An important issue to arrange while assigning semantic meaning to a combination of classes is the data fusion. Li and Bretschneider Li Bretschneider 2006 propose a method where combination of feature vectors for the interactive learning phase is carried out. They propose an intermediate step between region pairs clusters from k-means algorithm and semantic concepts called code pairs. To classify the low-level feature vectors into a set of codes that form a codebook the Generalised Lloyd Algorithm is used. Each image is encoded by an individual subset of these codes based on the low-level features of its regions. Signal classes are objective and depend on feature data and not on semantics. Chang et al. Chang et al. 2002 propose a semantic clustering. This is a parallel solution considering semantics in the clustering phase. In the article a first level of semantics dividing an image in semantic high category clusters as for instance grass water and agriculture is provided. Then each cluster is divided in feature subclusters as texture colour or shape. Finally for each subcluster a semantic meaning is assigned. In terms of classification of multiple features in an interactive way there exist few methods in the literature. Chang et al. Chang et al. 2002 describe the design of a multilayer neural network model to merge the results of basic queries on individual features. The input to the neural network is the set of similarity measurements for different feature classes and the output is the overall similarity of the image. To train the neural network .