Due to the wide application of Remote Sensing (RS) image scene classification, more and more scholars activate great attention to it. With the development of the Convolutional Neural Network (CNN), the CNN-based methods of RS image scene classification have made impressive progress. In the existing works, most of the architectures just considered the global information of the RS images. However, the global information contains a large number of redundant areas that diminish the classification performance and ignore the local information that reflects more fine spatial details of local objects. Furthermore, most CNN-based methods assign the same weights to each feature vector causing the mode to fail to discriminate the crucial features. In this paper, a novel method by Two-branch Deep Feature Embedding with a Dual Attention-Aware module for RS image scene classification is proposed. In order to mine more complementary information, we extract global semanticbased features of high level and local object-based features of low level by the Two-branch Deep Feature Embedding (TDFE) module. Then, to focus selectively on the key global-semantics feature maps as well as the key local regions, we propose a Dual Attention-Aware (DAA) module to attain those key information. We conduct extensive experiments to verify the superiority of our proposed method, and the experimental results obtained on two widely used RS scene classification benchmarks demonstrate the effectiveness of the proposed method.
The target tracking of nonlinear maneuvering radar in dense clutter environments is still an important but difficult problem to be solved effectively. Traditional solutions often rely on motion models and prior distributions. This paper presents a novel improved architecture of Kalman filter based on a recursive neural network, which combines the sequence learning of recurrent neural networks with the precise prediction of Kalman filter in an end-to-end manner. We employ three LSTM networks to model nonlinear motion equation, motion noise, and measurement noise, respectively, and learn their long-term dependence from a large amount of training data. They are then applied to the prediction and update process of Kalman filter to calculate the estimated target state. Our approach is able to address the tracking problem of nonlinear maneuvering radar target online end-to-end and does not require the motion models and prior distributions. Experimental results show that our method is more effective and faster than the traditional methods and more accurate than the method with LSTM network alone.
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