Terrain classifications is an important topic in polarimetric synthetic aperture radar (PolSAR) image processing and interpretation. A novel PolSAR classification method based on three-channel convolutional neural network (Tc-CNN) is proposed and this method can effectively take the advantage of unlabeled samples to improve the performance of classification with a small number of labeled samples. Several strategies are included in the proposed method. (1) In order to take the advantage of unlabeled samples, a data enhancement method based on neighborhood nearest neighbor propagation (N3P) method is proposed to enlarge the number of labeled samples. (2) To increase the role of central pixel in CNN classification based on pixel, a spatial weighted method is proposed to increase the weight of central pixel features and weak the weight of other types of pixel features. (3) A specific deep model for PolSAR image classification (named as Tc-CNN) is proposed, which can obtain more scale and deep polarization information to improve the classification results. Experimental results show that the proposed method achieves a much better performance than existing classification methods when the number of labeled samples is few.

The industrial Internet revolutionizes traditional manufacturing through the incorporation of technologies such as real-time production optimization, big data analysis, etc. Computing resource-constrained industrial terminals struggle to effectively execute latency-sensitive and computation-intensive tasks triggered by these technologies. Edge computing (EC) emerges as a promising paradigm for offloading tasks from terminals to the adjacent edge servers, offering the potentiality to augment the computational capacities for industrial terminals. However, the development of accurate offloading strategies poses a prominent challenge for EC in the industrial Internet. Incorrect offloading strategies will misguide the task offloading procedure, resulting in adverse consequences. In this paper, we study the latency-aware multi-server partial EC task offloading problem in the industrial Internet with the consideration of joining load balancing and security protection to provide accurate strategies. Firstly, we establish a task offloading model that supports partial offloading, facilitating latency reduction, task offloading across multiple edge servers with load balance, and accommodation of fuzzy task risks. We quantify the established model as a constrained optimization formulation and prove its NP-hardness. Secondly, to solve the composite offloading strategy comprising both the offloading location and offloading ratio derived from our model, we propose a bi-layer offloading algorithm with joint load balance and fuzzy security, which is based on the adaptive genetic algorithm and simulated annealing particle swarm optimization. Based on extensive experimental results, we find that the established model is effective in reducing the objective value, with a respective decrease of 27% and 46% compared to full execution in edge servers and local execution in industrial terminals. Furthermore, the proposed offloading algorithm exhibits superior performance in terms of solution accuracy compared to existing algorithms.

Recently, convolutional neural networks (CNNs) have been successfully utilized in polarimetric synthetic aperture radar (PolSAR) image classification and obtained promising results. However, most CNN-based classification methods require a large number of labeled samples and it is difficult to obtain sufficient labeled samples. For this reason, an ensemble dual-branch CNN (EDb-CNN) is proposed for PolSAR image classification with small samples. First, to solve the problem of the small sample in PolSAR image classification, a new data enhancement method based on the superpixel algorithm is proposed to expand the number of labeled samples. Second, to obtain different scales of features from PolSAR images, a Db-CNN model is proposed. This model contains two parallel CNN structures. One CNN branch is used to extract the polarization features from the complex coherency matrix. The other branch is utilized to extract the spatial features based on weighted spatial neighborhood. On the top of these two branches, a feature fusion model is adopted to combine these two deep features, and a weighted loss function is employed to improve the learning procedure. Then, the ensemble learning algorithm is used for each CNN branch and Db-CNN network to obtain the better classification results. Finally, a postprocess algorithm based on the superpixel algorithm is proposed to improve the consistency of classification results. Experiments on two PolSAR datasets show that the proposed method achieves a much better performance than other classification methods, especially when only a few labeled samples are available.

Polarimetric synthetic aperture radar (PolSAR) images classification is an important topic for PolSAR images understanding and interpretation. However, traditional pixel-based PolSAR image classification that takes image pixel as a processing unit cannot make full use of spatial information and, thus, may not obtain the satisfactory classification results. Hence, this letter proposed an attention-based multiscale sequential network for PolSAR images classification increasing the multiscale spatial information between pixels by way of spatial sequence. Specifically, the long short-term memory (LSTM) network is introduced to convert the time sequence into spatial sequence to extract the spatial features. Then, to obtain the more abundant spatial features and select more important spatial information, an attention-based multiscale spatial-enhanced LSTM (AMSE-LSTM) is proposed to enhance the relationship between pixel spatial information. Finally, a new mixed loss function is defined to improve the classification performance. Experimental results with two real PolSAR data show that compared with state-of-the-art methods, the proposed method can achieve a much better performance and overall classification accuracy.

Deep learning technology has rapidly advanced in the interpretation of polarimetric synthetic aperture radar (PolSAR) images in recent years. However, deep learning methods in PolSAR image interpretation primarily rely on a significant volume of labeled data to make precise predictions, while disregarding the potential physical features of PolSAR. To solve the problem, a deep fusion network is proposed in this letter, which can effectively utilize the complementary characteristics between amplitude and physical features of PolSAR images to enhance the interpretability of the network and improve PolSAR image classification performance. In addition, an improved feature pyramid network and a learnable feature fusion module were proposed to autonomously learn the required fused feature information and avoid the process of feature selection. Finally, the spectral features of PolSAR data are fused to further enhance the discriminability of the features extracted by the proposed network, and improve the classification accuracy of the proposed method. Additionally, to verify the effectiveness of the proposed method, two real PolSAR data sets were used. The experimental results demonstrate that the proposed method achieves higher accuracy, even with a limited number of labeled samples.