In the existing studies on remote sensing image scene classification, the supervised learning methods which are fine-tuned from pre-trained model require a large amount of labeled training data and parameters, while unsupervised learning methods do not make full use of label information, and the classification performance could be improved. In this paper, we introduced semi-supervised learning into generative adversarial network (GAN), so the discriminator learned more discriminative features from labeled data and unlabeled data. Moreover, the mixup data augmentation method was introduced into our classification model to augment the data and stabilized the training process. We carried out extensive experiments for both UC-Merced and NWPU-RESISC45 datasets with a 5-fold cross-validation protocol using a linear SVM as classifier. We trained the proposed method on UC-Merced dataset and achieve an average overall accuracy of 94.05% under 80% training ratio. When trained on NWPU-RESISC45 dataset, the proposed method reached an average overall accuracy of 83.12% and 92.78% under the training ratios of 20% and 80% respectively, which achieves the state-of-the-art deep learning methods without pre-training.

Synthesis of thinned linear array geometry with low redundancy is an important problem for one-dimensional aperture synthesis radiometers (ASRs) with high spatial resolution and satisfactory sensitivity. In this paper, a synthesis method of low-redundancy linear array (LRLA) with large numbers of antennas is proposed. Two LRLA patterns are suggested to significantly reduce the tremendous solution space of large LRLA search problem and a strategy is proposed to speed up exploration. Further, particle swarm optimization (PSO) algorithm exploiting the suggested patterns and strategy is applied to the array synthesis. The advantages of the proposed method, in terms of both generating more new large LRLA configurations and reducing computational cost for the large LRLA configuration synthesis problem, are demonstrated by comparing its performance with that of the previously proposed methods. Finally, a number of newly found large LRLA configurations for 28-37 elements are also presented.

Double difference bases (DDBs) and construction of thinned linear arrays with twofold redundancy are discussed in this communication. We present an analytical method, based on the combinatorial tools, to generate twofold redundancy arrays by employing regular DDB patterns separately or combining them with cyclic difference sets. Besides, the bound for DDBs is derived that would be useful to research on thinned array geometries.

This communication addresses the thinned linear array design for the optimization of radiometric sensitivity and angular resolution, both of which are crucial characteristics of interferometric aperture synthesis radiometers (IASRs). First, we establish a multi-objective optimization model concerning their tradeoff. Then, three state-of-the-art search algorithms, namely, multi-objective particle swarm optimization (MOPSO), nondominated sorting genetic algorithm II (NSGA-II), and nondominated neighbor immune algorithm (NNIA), are implemented for the design of linear interferometric arrays. Further, a constraint principle of interferometric arrays (CPIA) is proposed to reveal the inherent relation among array parameters closely related to IASR performances, which would be useful for the multi-objective optimization of IASR arrays.

Biometrics identification uses human biometric such as human faces, fingerprints, irises for authentication. Electroencephalograph (EEG) provides a detectable neuroelectrophysiological signal that could reflect psychological or behavioral characteristics. In the past, EEG -based biometrics were usually used with machine learning based procedure for identification. Recently Convolutional Neural Networks (CNN) was used as a new tool for biometric automatic feature exaction and classification. This paper aims at investigating CNN's performance on EEG -based human identification with the increase number of subjects. P300 -speller was introduced in this study to induce Event -related potential (ERP). 33 healthy subjects participated in the experiment. Result shows that the CNN -based Biometric System reached a high degree of accuracy (99.9%) after for 8 -class classification , 99.3% for 10 -class and 99.3% for 13 -class. Then, we made some finetuning to the network structure of the 10 -class and the 13 class classification, Then,we also improved the convergence speed of the network and reduced the time to get the best classification model by changing the structure of the CNN that we introduced here, while ensuring that the accuracy and the loss function did not change significantly. The finding indicts that CNN get good performance in EEG -based biometric identification.