LAUSR

With the development of society, intelligent urban construction is drawing attention globally. It embed sensors and equipment into various environmental monitoring target objects to achieve environmental management and decision-making wisdom in a more granular and dynamic manner. However, how to achieve target objects’ recognition in the dynamic environment is of essential importance for intelligent urban construction. Due to the shape, color and other characteristics for target objects are more similar, which make it difficult to identify the target types based on the low-level features such as color, shape, etc. In this paper, we attempt to apply the deep neural network composed of sparse autoencoders based unsupervised feature learning to identify the various types of target objects. On the other hand, due to the fact that the quantities of target objects which are obtained by environmental monitor may be not sufficient, cannot get more high-level visual feature through feature training, which affects the accuracy of the subsequent target recognition. A cross-domain feature learning scheme for target objects recognition using convolutional sparse auto-encoder has been presented. In order to improve the recognition speed, feature weights selection method based on a correlation analysis is further proposed for the purpose of reducing the amount of global features which are taken from target-domain images. Experimental results show that compared with non-transfer feature learning algorithm and underlying visual feature recognition algorithm, the new algorithm proposed in this paper has higher accuracy and robustness. Feature selection can reduce the computational time of global feature extraction and recognition by about 30% while improving recognition performance.