Superpixel-based graph convolutional network (SGCN) has shown the advantages of less computational time and global modeling ability for polarimetric synthetic aperture radar (PolSAR) image classification. However, the effectiveness is heavily dependent on the superpixel segmentation result. Existing superpixel segmentation methods usually produce edge errors due to speckle and scattering confusion, which directly results in the mistakes of the final classification. To address this issue, a novel hybrid weighted fuzzy SGCN method(HF-SGCN) is proposed to correct the edge pixels by defining a fuzzy projection matrix (FPM). The FPM can transform features from superpixel to pixel, by which features of edge pixels can be calculated from all the neighboring superpixels with a certain probability, so as to correct edges to the most similar region. In addition, a hybrid weighted adjacent matrix is formulated by incorporating both the revised Wishart and multi-feature distances, which can enhance the discriminating features effectively. The proposed HF-SGCN method is capable of capturing the global contextual information and rectifying edges, while disregarding the local individual features for each pixel. To combine global and local features, we further propose the HF-SGCN-CNN method, which integrates the superpixel-wise HF-SGCN network and the pixel-wise 3D-CNN network into a unified framework. Thus, we can fuse the features extracted from two subnetworks, producing complementary global and local features that significantly improve classification accuracy. Experiments are conducted on four publicly real PolSAR datasets with different sensors and bands. Experimental results demonstrate the effectiveness of the proposed methods.

State-of-Health (SOH) is critical to managing the lifespan of the battery energy storage system. For the data-driven-based method, explicit features have many benefits, but they cannot be constructed automatically. To maximize the use of explicit features and automate its construction and other configuration processes, this article design a unified optimization paradigm for synergizing the three fundamental procedures in the data-driven model, that is, feature extraction, importance assignment, and model parameterization. An Evolutionary Multi-Objective (EMO) method is used to synchronously find a series of non-dominated solutions for the best combination of features, attention layer, and hyper-parameters of the network, which can enable a flexible SOH estimation in various operation conditions. A piecewise aggregate approximation is designed to compress the partial voltage curve while keeping its tendency characteristics. A Long Short-Term Memory (LSTM) is used to establish the data-driven model, especially an attention layer is added to finalize the task of feature selection. Experimental results prove the proposed model can achieve accurate SOH estimation and also provide flexible solutions for different scenarios. And results with multi-battery validation and transfer learning demonstrate that the proposed method not only has a high generalization ability but also easily transfers to a new task.

Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important study areas for PolSAR image processing. Many kinds of PolSAR features can be extracted for PolSAR image classification, such as the scattering, polarimetric or image features. However, it is difficult to improve the classification accuracy of PolSAR images by using all these low-level features directly, since they may conflict with each other for classification. Hence, how to joint learn these low-level features to obtain high-level discriminating features is a challenging task. To solve this problem, a novel fast multi-feature joint learning method(fMF-JLC) is proposed for PolSAR image classification. The proposed method extract three kinds of low-level features of PolSAR data at first. Then, a multi-feature joint sparse representation model(MF-JSR) is proposed by designing joint sparse constraints on the extracted features above. Moreover, the joint sparse features are further compressed to overcome the dimension curse and acquire semantic features by the topic model. By this way, the low-level features are fused and discriminating high-level features are acquired. However, the pixel-wise feature learning method is time consuming. To speed the proposed method, a superpixel-based fast learning method is designed by involving the contextual relationship. Experiments are taken on three sets of real PolSAR data with different sensors and bands, and several compared methods are used to verify the effectiveness of the proposed method. The experimental results illustrate that the proposed method can obtain better performance than the state-of-art methods, especially for the heterogenous areas.

Deep learning methods excel in Polarimetric SAR (PolSAR) image classification. However, existing methods typically sample an image block for each pixel with a fixed-size square window, which always contains inconsistent/incomplete content with the central pixel, resulting in many misclassifications especially in boundary and heterogeneous regions. So, a size-fixed square window is not enough for representing various terrain objects. To address this issue, we develop a content-adaptive multi-region deep network to obtain contextual consistent sampling windows for diverse terrain objects. Firstly, a complex scene of PolSAR image is partitioned into homogeneous, heterogeneous and boundary regions. Then, sampling windows with adaptive direction and scale are designed for three distinct regions. Besides, windows with central and global regions are proposed to provide additional local and global information. Finally, a fusion network is designed to adaptively combine different sampling windows to enhance classification performance. Experimental results on three real data sets demonstrate that the proposed method can achieve superior performance in both edge details and heterogeneous terrain objects compared with the state-of-the-art methods.