Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided into two categories, namely pixels-based methods and superpixels-based methods. Pixels-based semi-supervised methods are liable to be affected by speckle noises and have a relatively high computational complexity. Superpixels-based methods focus on the superpixels and ignore tiny detail-preserving represented by pixels. In this paper, a Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed. To reduce the effect of speckle noises and preserve the details, FS-SCNN uses a fuzzy superpixels algorithm to segment an image into two parts, superpixels and undetermined pixels. Moreover, the fuzzy superpixels algorithm can also reduce the number of mixed superpixels and improve classification performance. To exploit unlabeled data effectively, we also propose a Similarity-constrained Convolutional Neural Network (SCNN) model to assign pseudo labels to unlabeled data. The final training set consists of the initial labeled data and these pseudo labeled data. Three PolSAR images are used to demonstrate the excellent classification performance of the FS-SCNN method with data of limited labels.

Video generation has become an increasingly important component of AI-generated content (AIGC), owing to its rich semantic expressiveness and growing application potential. Among various generative paradigms, diffusion models have recently gained prominence due to their strong controllability, competitive visual quality, and compatibility with multimodal inputs. However, most existing surveys provide limited coverage of diffusion-based video generation, often lacking systematic analysis and comprehensive comparisons. To address this gap, this paper presents a thorough and structured review of diffusion models for video generation. We first outline the theoretical foundations and core architectures of diffusion models, and then the key design principles of representative methods for video generation were introduced. We propose a unified taxonomy that categorizes over two hundred methods, analyzing their key characteristics, strengths, and limitations. In addition, we compared the performance of classical methods and summarized commonly used datasets and evaluation metrics in this field for ease of model benchmarking and selection. Finally, we discuss open problems and future research directions, aiming to provide a valuable reference for both academic research and practical development.

Dimensionality reduction is of great importance which aims at reducing the spectral dimensionality while keeping the desirable intrinsic structure information of hyperspectral images. Tensor analysis which can retain both spatial and spectral information of hyperspectral images has caused more and more concern in the field of hyperspectral images processing. In general, a desirable low dimensionality feature representation should be discriminative and compact. To achieve this, a tensor discriminant analysis model via compact feature representation (TDA-CFR) was proposed in this paper. In TDA-CFR, the traditional linear discriminant analysis was extended to tensor space to make the resulting feature representation more informative and discriminative. Furthermore, TDA-CFR redefines the feature representation of each spectral band by employing the tensor low rank decomposition framework which leads to a more compact representation.