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 (IFPN) and a learnable feature fusion module (LFFM) 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. In addition, to verify the effectiveness of the proposed method, two real PolSAR datasets were used. The experimental results demonstrate that the proposed method achieves higher accuracy, even with a limited number of labeled samples.

With the development of science and technology, although more and more polarimetric synthetic aperture radar (PolSAR) data are collected, marking PolSAR data still requires a lot of costs. Moreover, the datasets between different domains have the class distribution shift problem, which reduces the reusability of labeled samples between cross-domain images. To address this issue, this article proposed an unsupervised domain adaptation (UDA) network based on coordinate attention (CA) and weighted clustering. Firstly, an adversarial UDA network with a bi-classifier is introduced to eliminate the problem of class distribution shift and achieve alignment of data distribution between different domains. Secondly, the CA mechanism is introduced to select important features to enhance the utilization of spatial information among pixels. Finally, to improve the utilization of semantic and classification information of the target domain, and to align same class samples, a weighted clustering algorithm is introduced. Experimental results show that compared with the existing UDA method, the proposed method can achieve the better PolSAR image classification.

In this paper, we proposed an attention-based deep sequential network (ADSN) for PolSAR images classification increasing the 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, a spatial enhanced strategy is carried out to enhance the relationship between pixel spatial information based on LSTM. Finally, to avoid feature selection procedures, the attention mechanism is introduced in LSTM network to select the important information and improve the classification performance. The experiments clearly demonstrate that compared with state-of-art methods, the proposed method can achieve a much better performance and overall Classification accuracy.

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.

This paper presents a new dual-channel convolutional neural network (Dc-CNN) for Polarimetric synthetic aperture radar (PolSAR) image classification when labeled samples are small. First, a neighborhood minimum spanning tree (MST) is used to enlarge the labeled sample set. Then, in order to obtain the abundant spatial information, a new dual-channel CNN is designed to PolSAR image to acquire different spatial features. This network model contains two parallel CNN structures, which can extract different features used two multiscale convolution structure. Experiments results show that compared with other methods, the proposed method shows a satisfactory classification result.

This paper presents an establish and operation of energy saving LEDs virtual international research/education center. This is a long-term international engineering education and research collaboration program among California Polytechnic State University (Cal Poly), United States, Peking University (PKU), Beijing, China, and Tsinghua University, Beijing, China on light emitting diode (LED) research in the past seven years. We focused on GaN laser diode (LD) research for the first year with PKU. Then GaN light emitting diode (LED) research was added during the second year. In 2012, we expanded our interest to organic light-emitting diodes (OLEDs) and collaborated with Tsinghua University. The project began by having faculty from Cal Poly to work in PKU for one summer. The collaboration in the rest of the period was done through teleconference and e-mails. Cal Poly graduate students were grouped with graduate students in China and worked closely on certain projects. Through this project, our students (US and Chinese) obtained experience in collaborating with foreign partners, especially awareness of cultural differences, without traveling abroad in most of the time.