The remote sensing scene classification (RSSC) based on convolutional neural networks (CNNs) are generally limited by the complex background interference and the difficulty of identifying key targets in image. Thus, this letter proposes a semantic-aware fusion network for RSSC, abbreviated as SAF-Net, to better construct discriminative features and effectively fuse features for classification. The proposed SAF-Net, which employs the ResNet50 pretrained on the ImageNet dataset as the backbone network, mainly contains the semantic-aware module and the multilayer feature fusion module (MFFM). The semantic-aware module utilizes a spatial enhanced module (SEM) and a covariance channel attention module (CCAM) to accurately capture the discriminative semantic features. It can precisely identify and extract the essential semantic elements in image, such as distinct object types and their spatial distributions. Then, the MFFM uses the features learned by the semantic-aware module to guide other layers for effective feature fusion through a self-attention mechanism. It can not only enriches the feature representation of SAF-Net but also ensure the effective fusion of the semantic information. Extensive comparisons and ablation experiments on remote sensing datasets demonstrate the effectiveness of the proposed SAF-Net, and verify that it can greatly improve the classification performance.

For maintaining the phase information in images, complex neural networks have been widely applied to PolSAR classification. However, due to constant weights of neurons, the networks may lack randomness and be potentially overfitting for complicated imaging mechanisms and random speckle noise in PolSAR images. Thus, this letter proposes a complex variational inference network (CVIN) where complex Gaussian probability distributions are introduced into the weights of neurons in complex neural networks. In CVIN, a novel evidence lower bound (ELBO) for complex network is designed to infer the variational approximation of weights through backpropagation. After training, CVIN propagates the approximate posterior distributions given the data and makes the prediction of the labels. Thus, CVIN is an ensemble of flexible models with infinite weights, where the complex weights are regularized by the Gaussian distributions. Experiments on real PolSAR images verify the feasibility of CVIN and illustrate the potential of CVIN to serve as a competitive method for PolSAR classification.

Deep learning has powerful feature extraction abilities and has achieved promising results in polarimetric synthetic aperture radar (PolSAR) image classification. However, the labeled samples of PolSAR images are generally limited, which could lead to the overfitting of deep networks and the inefficiency of deep features. To overcome this problem, in this article, we propose a complex-valued enforcing population and lifetime sparsity (CV-EPLS) model to extract nonredundant sparse features from PolSAR images. CV-EPLS achieves unsupervised learning of sparse polarimetric features with limited and unlabeled samples, including amplitude and phase information in multiple polarimetric channels. Concretely, CV-EPLS defines an activation metric function to achieve strong population sparsity. Additionally, a grid search strategy is designed to ensure that activation items are evenly distributed among the sparse targets, thus forming strong lifetime sparsity. In this way, CV-EPLS constructs the complex sparse matrices and extracts discriminative sparse features in an unsupervised way, with the dependence of features being effectively reduced. Experimental results on PolSAR images demonstrate the effectiveness of CV-EPLS in the extraction of features and its application to image classification.

Deep learning (DL) methods usually need to collect a large amount of labeled data to extract deep features. However, due to the difficulty of obtaining numerous labeled data from synthetic aperture radar (SAR) images, unsupervised feature learning has been focused on SAR image processing. In this paper, we propose a three-dimensional sparse model (3-DSM) to extract deep sparse features from SAR images in an unsupervised way. Concretely, 3-DSM learns the convolution kernels by minimizing the error between the features and the constructed sparse maps, without labeled samples. Thus, the discriminative features can be extracted in an unsupervised way by the learned convolution kernels and are able to capture the main structure information of SAR images. Furthermore, to the best of our knowledge, 3-DSM firstly specifies the sparsity of convolution kernels, with each convolution kernel exhibiting its independence from the others and the redundancy of convolution kernels being diminishing. It means that each convolution kernel extracts its unique structural features of SAR images. Consequently, in the feature extraction, three-dimensional sparsities have been specified, including width, height, and depth, with the acquisition of discriminative less-redundant features. The effectiveness of 3-DSM is demonstrated by the feature extraction and segmentation of the simulated and real SAR images.

Conditional random fields (CRFs) model is suitable for image segmentation because it can capture the dependencies of observed data and incorporate the spatial correlations into the segmentation process. In this article, to deal with the segmentation of nonstationary synthetic aperture radar (SAR) image, we combine the modeling power of the CRF model with the representation-learning ability of principal component analysis network (PCANet), and thus propose a high-order triplet CRF model based on PCANet (HOTCRF-PCANet). HOTCRF-PCANet introduces an auxiliary field to explicitly regulate nonstationary label structure patterns. Under the guidance of this auxiliary field, HOTCRF-PCANet defines a discrete quadrilateral nonstationary Markov fields model, and thus considers both the nonstationary property of image and high-order label interactions. In addition, guided by the auxiliary field, HOTCRF-PCANet proposes to use a product-of-expert (POE) potential to enforce the regions’ labeling consistency for pixels within the weak-structured region. To automatically learn rich feature representations, HOTCRF-PCANet modifies PCANet into an unsupervised mode, i.e., unsupervised PCANet (UPCANet), and constructs an UPCANet-based unary potential to effectively predict the local class probability. The effectiveness of HOTCRF-PCANet is demonstrated by the application to the unsupervised segmentation of the simulated images and real SAR images.

In this paper, we combine the modeling power of conditional random fields (CRF) model with the representation-learning ability of principal component analysis network (PCANet), and propose a high-order triplet CRF model, named as HOTCRF-PCANet, for unsupervised synthetic aperture radar (SAR) image segmentation. HOTCRF-PCANet introduces an auxiliary field to explicitly regulate label interactions of complex SAR image. In the label and auxiliary fields, HOTCRF-PCANet defines a discrete quadrilateral pairwise Markov fields (DQPMF) model, and thus constructs a high-order DQPMF potential to model the high-order label interactions in an unsupervised way. Additionally, HOTCRF-PCANet uses a product-of-expert (POE) potential to enforce the regions' labeling consistency for pixels within the weak-structured region. Moreover, HOTCRF-PCANet modifies PCANet into an unsupervised mode, i.e. UPCANet, automatically learns rich features of SAR image and constructs an UPCANet-based unary potential to predict the local class probability. The effectiveness of HOTCRF-PCANet is demonstrated by the application to the unsupervised segmentation of simulated and real SAR images.

Electronic medical record (EMR) system has become increasingly more important in developed countries due to its convenience and efficiency in medical information storage, management and analysis. However, one of the main limitations of EMR lies in that the clinical data for patients cannot be exchanged among different medical institutions. Recently, the cloud-based clinic system, featuring in lower cost and stronger functionality in data integration, has gained more and more attention. The cloud-based clinic system fulfills the requirement of “big data”, allowing higher efficiency and accuracy in data mining and analysis which may significantly improve the medical care and management. In this study, we propose a cloud-based EMR system integrated with recommender functionality. The recommender system features in both drug recommendation and decision support in diagnosis. In this paper, experiments to test ranking of recommended drugs and the auxiliary diagnosis support are presented. Results demonstrated good performance for both tests. In the future, the clinical decision support in diagnosis will be further improved with the knowledge from the professional medical providers.