Recently, the Mamba architecture based on State Space Models (SSMs) has gained attention in 3D human pose estimation due to its linear complexity and strong global modeling capability. However, existing SSM-based methods typically apply manually designed scan operations to flatten detected 2D pose sequences into purely temporal sequences, either locally or globally. This approach disrupts the inherent spatial structure of human poses and entangles spatial and temporal features, making it difficult to capture complex pose dependencies. To address these limitations, we propose the Skeleton Structure-Aware Stride SSM (SAS-SSM), which first employs a structure-aware spatiotemporal convolution to dynamically capture essential local interactions between joints, and then applies a stride-based scan strategy to construct multi-scale global structural representations. This enables flexible modeling of both local and global pose information while maintaining linear computational complexity. Built upon SAS-SSM, our model SasMamba achieves competitive 3D pose estimation performance with significantly fewer parameters compared to existing hybrid models. The source code is available at https://hucui2022.github.io/sasmamba_proj/.

Transformers have been widely adopted in the field of hyperspectral image (HSI) classification. However, a significant drawback of transformers lies in their excessive number of parameters and the high computational overhead. To address this challenge, we propose a Channel-Reduced Transformer (CRFormer) for HSI classification. In an effort to enhance computational efficiency, we first introduce a cross-region tokenization (CRT) approach. This method effectively shortens the sequence length input to the transformer, thereby alleviating the computational burden. Additionally, we propose a channel-reduced multi-head self-attention (CR-MHSA) module. This module operates on only half of the input channels while still attaining comparable or even superior results. Experimental results conducted on three benchmark datasets demonstrate that our proposed method not only achieves superior classification accuracy but also significantly reduces computational complexity compared to other transformer-based approaches.

Cross-domain few-shot learning (FSL) has demonstrated remarkable new classes recognition capabilities in hyperspectral image classification tasks. However, existing domain adaptation methods face two critical challenges in the cross-domain feature alignment process: first, the domain shift leads to misaligned feature transfer and diminished classification accuracy; second, the intra-class feature dispersion and inter-class boundary blurring in few-shot tasks result in degraded classification performance for novel classes. Moreover, the impact of redundant and noisy data on model discriminability is rarely considered in existing approaches. To solve these issues, this article proposes a cross-domain FSL hyperspectral image classification method based on diffusion-augmented prototype knowledge distillation (DAPKD-CFSL). Firstly, we introduce a diffusion-augmented unsupervised domain adaptation pre-training (DA-PT) framework to address the domain shift by performing a domain-adversarial denoising and reconstruction task using visible source data and masked target data. Second, our dual-branch spatial-spectral attention (DB-SSA) captures global and local spectral-spatial dependencies to enhance feature representation. Then, the proposed global-local prototype knowledge distillation (GL-PKD) performs global prototype alignment while conducting local contrastive learning, addressing feature dispersion and boundary ambiguity. Finally, a dynamic learning strategy prioritizes feature alignment early and gradually strengthens classification supervision through adaptive loss weights, and incorporates an SNR-enhanced loss to effectively mitigate noise interference. The experimental results on three HSI datasets demonstrate the superiority and effectiveness of the proposed DAPKD-CFSL.

Diffusion model has achieved excellent performance in natural image processing, which can learn the noise distribution by the degradation and restoration processes. However, the model is limited to Gaussian noises. Actually, Polarimetric Synthetic Aperture Radar(PolSAR) images have complex non-Gaussian speckle noises, for which the Gaussian diffusion model is difficult to learn their intrinsic statistical characteristics. In this paper, we propose a novel scattering mechanism inspired non-Gaussian diffusion model for PolSAR image classification. To better simulate the PolSAR speckle noise, a mixed noise distribution is defined for PolSAR covariance matrices by combining Gamma multiplicative and Gaussian additive noises. A non-Gaussian forward noising process is derived to degrade a clean PolSAR image to a noisy image by steps. Then, the U-net structure is trained to remove noises for each step, effectively extracting non-Gaussian statistical features. However, statistical features can only characterize the overall distribution of the dataset, which is insufficient to describe complicated individual objects; the original PolSAR data reflect the detailed scattering mechanism for individual pixels, which can provide complementary object information for classification. Therefore, a scattering-statistical joint learning network is further developed with a dual-branch architecture to enhance discrimination ability. In particular, a multiscale pyramid module and attention mechanism are designed to improve the ability of feature learning. Experimental results on five real PolSAR datasets demonstrate that the proposed method effectively captures edge details and preserves homogeneous regions for terrain classification, especially in heterogeneous regions.

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.

According to the small sample characteristics of polarimetric synthetic aperture radar (PolSAR) data and its unique data attributes, a new network architecture for PolSAR images classification based on Unet is proposed in this paper. Fully considering the characteristics of PolSAR data, the spatial features and channel features of the input data are extracted respectively by the depthwise separable convolution and avoid extracting redundant features. In order to improve the classification accuracy, the residual structure is used to increase the depth of the network and fully transmit the characteristics information of PolSAR data. The experimental results clearly demonstrate that the architecture we proposed can achieve better classification accuracy than other PolSAR images classification methods.

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.

In this article, the terrain classifications of polarimetric synthetic aperture radar (PolSAR) images are studied. A novel semi-supervised method based on improved Tri-training combined with a neighborhood minimum spanning tree (NMST) is proposed. Several strategies are included in the method: 1) a high-dimensional vector of polarimetric features that are obtained from the coherency matrix and diverse target decompositions is constructed; 2) this vector is divided into three subvectors and each subvector consists of one-third of the polarimetric features, randomly selected. The three subvectors are used to separately train the three different base classifiers in the Tri-training algorithm to increase the diversity of classification; and 3) a help-training sample selection with the improved NMST that uses both the coherency matrix and the spatial information is adopted to select highly reliable unlabeled samples to increase the training sets. Thus, the proposed method can effectively take advantage of unlabeled samples to improve the classification. Experimental results show that with a small number of labeled samples, the proposed method achieves a much better performance than existing classification methods.

Terrain classification plays an important role in understanding Polari- metric Synthetic Aperture Radar (PolSAR) image intuitively. In the process of classification, feature extraction is critical. However, the preprocess of speckle noise filtering affects the effectiveness of the feature extractor which influences the accuracy of classification ultimately. Thus we integrate de-noising process and classification into an end-to-end framework based on CNN termed as Denoising-CNN, which improves the accuracy of classification. Experiments on real PolSAR data show that our proposed method offers an excellent performance.

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.

With the great successful development of deep learning, stacked auto-encoder (SAE) has been widely used in POLSAR image terrain classification. In this paper, we propose a complex-valued Wishart stacked auto-encoder (CV-WSAE) classification model for POLSAR data interpretation. The proposed method stacks a complex-valued Wishart autoencoder (CV-WAE) and a complex-valued auto-encoder (CVAE) for feature extraction and connects a linear classifier for image classification. It not only expends real-valued neural network to complex-valued, but also utilizes the statistical distribution of POLSAR image. What is more, all elements of CV-WSAE including input, hidden, output, encoder and decoder layers are complex-valued, and a complex back propagation algorithm is used for training processing. The experiments of a real POLSAR data illustrate that this method can obtain good classification accuracy.

Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application. In the last few years, superpixels are widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, no superpixel algorithm is especially designed for image classification. It is believed that both mixed superpixels and pure superpixels exist in an image. Nevertheless, mixed superpixels have negative effects on classification accuracy. Thus, it is necessary to generate superpixels containing as few mixed superpixels as possible for image classification. In this paper, first, a novel superpixels concept, named fuzzy superpixels, is proposed for reducing the generation of mixed superpixels. In fuzzy superpixels, not all pixels are assigned to a corresponding superpixel. We would rather ignore the pixels than assigning them to improper superpixels. Second, a new algorithm, named FuzzyS (FS), is proposed to generate fuzzy superpixels for PolSAR image classification. Three PolSAR images are used to verify the effect of the proposed FS algorithm. Experimental results demonstrate the superiority of the proposed FS algorithm over several state-of-the-art superpixels algorithms.

In this paper, we proposed a new semi-supervised method for polarimetric synthetic aperture radar (PolSAR) terrain classification based on improved tri-training. This method only needs a few numbers of labeled samples to achieve the results obtained by traditional supervised classification methods. First, it uses a variety of target decomposition methods to obtain high-dimensional feature. Second, a new feature selection method based on the ratio of between-class scatter and within-class scatter is proposed to reduce the redundant feature. Finally, an improved tri-training method is executed. A real PolSAR data is used to verify the proposed method. Experimental results show that the proposed method is efficient with a few labeled samples and effectively improve the classification accuracy compared with other traditional classification methods.

In this paper, we present a new unsupervised classification method based on a novel polarization feature, which reflects the proportion of co-polarization component and cross-polarization component of scatters in PolSAR image. We combine this novel polarization feature with backscattering power and scattering power entropy to perform the initial classification. Then apply a merge criterion to merge clusters into the desired number of clusters. After each step, the complex Wishart clustering is performed to refine the classification results. Compared with the other three methods, the effectiveness of the proposed approach is demonstrated on NASA/JPL AIRSAR L-band data of San Francisco Bay.