Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.
The sweeping camera systems in the surveying and mapping industry are usually efficient in image acquisition, for the photography coverage of a single strip is relatively large. The triangulation angle of correspondence is overly tiny to adopt traditional block adjustment (BA). This study analysed the imaging principle of the Chinese APS7K comprehensive camera system and proposed an aerial triangulation method for the data this system acquired. The proposed method first determines the adjacent matrix from the POS data and trajectory information. The other part of the method is to overcome the weak relative geometry in a single strip by introducing the digital elevation model (DEM) data into the block adjustment scheme. The optimal solution of adjustment is obtained by iteratively solving the problem. We verified the optimisation's effectiveness by checking stitched orthophoto and check points from Google Earth. The results show that mosaic discrepancy is eliminated, the reprojection error is reduced to subpixel level, and positioning accuracy is better than 0.4 meter (ground sample distance is 0.2 meter) after adjustment with ground control points. Finally, the method’s shortcomings and prospects are summarized.
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