Semi-Supervised medical image segmentation aims to improve model performance with minimal expert annotations, yet it faces challenges in maintaining consistent and high-quality learning. Excessive perturbations can distort the model’s predictions and disrupt the decision boundaries, particularly in regions with uncertain predictions. In this paper, we introduce Confidence-Aware Adaptive Displacement (CAD), a framework that selectively identifies and replaces the largest low-confidence regions with high-confidence patches. CAD dynamically adjusts both the maximum replacement size and the confidence threshold during training, progressively refining segmentation quality while avoiding overwhelming the learning process. Experimental results on public medical datasets demonstrate that CAD significantly enhances segmentation performance, achieving new state-of-the-art accuracy in this field.

The advancement of spaceborne hyperspectral remote sensing technology has led to the widespread use of hyperspectral imaging, due to its ability to detect subtle spectral differences. Most of the traditional machine learning (ML) methods and popular deep learning (DL) architectures for hyperspectral image (HSI) classification either fail to capture global features or demand high computational resources. While multilayer perceptron (MLP)-based models offer a computationally efficient alternative, they struggle to capture manifold structures and are susceptible to overfitting. To address these challenges, we propose a novel spatial-spectral hypergraph dynamic gating MLP (S2H-DGMLP) framework tailored for HSI classification. The spatial–spectral hypergraph enhances discriminative power by modeling high-order spatial and spectral correlations, jointly optimizing local spatial features and global spectral features to produce more separable feature representations in the embedding space. Within this framework, the channel and spatial projections are statically parameterized using MLP, while the dynamic gating MLP (DGMLP) block captures global contextual information. The dynamic gating mechanism within the DGMLP block automatically adjusts the segmentation ratio to balance spatial and spectral contributions, while incorporating complex nonlinear combinations to improve feature representation. Experimental results on the Pavia University and Houston datasets demonstrate that S2H-DGMLP significantly improves classification performance, confirming its effectiveness in HSI classification tasks.

Linear regression and its variants have achieved considerable success in image classification. However, those methods still encounter two challenges when dealing with hyperspectral image (HSI) classification. On the one hand, the existing ones focus on mining the relationship between the label space and original data space during the classifier training, which is generally sensitive to noise corruptions. On the other hand, transforming the training samples into a strict binary label matrix makes the generalization ability of the classifier limited. To address these challenges, this paper constructs a novel integrative model called tensor decomposition-based relaxed linear regression (TDRLR) for HSI classification. Firstly, the model adopts tensor canonical polyadic (CP) decomposition to learn two dictionaries from spatial and spectral directions respectively, which can help to generate a double dictionary representation for HSI data. Then, the linear regression classifier is integrated to learn a transformation that reveals the mapping relation between the double dictionary representation and label space rather than the original data for enhancing robustness. Meanwhile, a more flexible way, label relaxation, is employed to enlarge the margins between different classes. More importantly, the learned double dictionary representation and classifier can be fine-tuned in tandem to enhance performance through the designed alternate iterative jointly learning algorithm. Experiments conducted on four real-world HSI datasets demonstrate that the proposed method achieves significant improvements in classification performance with a small size training set, when compared with state-of-the-art HSI classification methods.

Recently, linear regression has been a popular image classification technique since it is efficient and easy to implement. However, the current traditional regression methods require to convert samples to vectors even if the hyperspectral image (HSI) has a three-dimensional structure. Furthermore, the traditional linear regression methods can not fully utilize the spatial information in HSI data, which plays an important role in HSI classification. To address these problems, this paper proposes a novel dual linear regression in tensor space (TSDLR) for HSI classification. Specifically, the subspace representation of the HSI data is generated via tensor decomposition-based regression and the ridge regression is adopted to reveal the potential relationship between the subspace representation and label information. More importantly, the intrinsic spectral-spatial information of HSI is well discovered for classification by the tensor decomposition-based regression. The experimental results on two real HSI datasets demonstrated the effectiveness of the proposed TSDLR method.

Abstract Although low‐rank representation (LRR)‐based subspace learning has been widely applied for feature extraction in computer vision, how to enhance the discriminability of the low‐dimensional features extracted by LRR based subspace learning methods is still a problem that needs to be further investigated. Therefore, this paper proposes a novel low‐rank preserving embedding regression (LRPER) method by integrating LRR, linear regression, and projection learning into a unified framework. In LRPER, LRR can reveal the underlying structure information to strengthen the robustness of projection learning. The robust metric L 2,1 ‐norm is employed to measure the low‐rank reconstruction error and regression loss for moulding the noise and occlusions. An embedding regression is proposed to make full use of the prior information for improving the discriminability of the learned projection. In addition, an alternative iteration algorithm is designed to optimise the proposed model, and the computational complexity of the optimisation algorithm is briefly analysed. The convergence of the optimisation algorithm is theoretically and numerically studied. At last, extensive experiments on four types of image datasets are carried out to demonstrate the effectiveness of LRPER, and the experimental results demonstrate that LRPER performs better than some state‐of‐the‐art feature extraction methods.

The curse of dimensionality and noise corruption are two tough problems that need to be solved in hyperspectral image (HSI) classification. However, the current feature dimensionality reduction methods, including both feature extraction and feature selection ones, cannot simultaneously solve the above two problems well. To address this issue, this paper proposes a novel method calledL2,p-norm-based robust embedding regression (L2,p-RER) for robust feature dimensionality reduction of HSI, which can effectively suppress the impact of noises and reduce the feature dimensions. Specifically,L2,p-RER first integrates projection learning with robust principle component analysis (RPCA) to remove noise in a low-dimensional space. Secondly, an embedding regression regularization is proposed to improve the discriminability of the extracted low-dimensional features. Thirdly, aL2,1-norm constraint is imposed to improve the interpretability of the learned projection matrix, which can jointly extract the key features from all bands with their physical meanings certainly preserved. Last but most important, theL2,p-norm that can adaptively balance the sparsity and the convexity is employed to model the noise and regression residual in the embedded low-dimensional space, which can further enhance the robustness and generalization of the proposed method. In addition, extensive experiments conducted on three benchmark HSI datasets validated the effectiveness of the proposed method.

Subspace learning has been widely applied for feature extraction of hyperspectral images (HSIs) and achieved great success. However, the current methods still leave two problems that need to be further investigated. First, those methods mainly focus on finding one or multiple projection matrices for mapping the high-dimensional data into a low-dimensional subspace, which can only capture the information from each direction of high-order hyperspectral data separately. Second, the performance of feature extraction is barely satisfactory when the hyperspectral data is severely corrupted by noise. To address these issues, this article presents a t-linear tensor subspace learning (tLTSL) model for robust feature extraction of HSIs based on t-product projection. In the model, t-product projection is a new defined tensor transformation way similar to linear transformation in vector space, which can maximally capture the intrinsic structure of tensor data. The integrated tensor low-rank and sparse decomposition can effectively remove the noise corruption and the learned t-product projection can directly transform the high-order hyperspectral data into a subspace with information from all modes comprehensively considered. Moreover, a proposition related to tensor rank is proofed for interpreting the meaning of the tLTSL model. Extensive experiments are conducted on two different kinds of noise (i.e., simulated and real noise) corrupted HSI data, which validate the effectiveness of tLTSL.

Since hyperspectral image (HSI) is naturally presented as 3D data cube, patch tensor-based graph embedding methods have been widely applied for dimensionality reduction (DR) of HSI. However, these methods are usually developed with the patch tensors generated by the raw data which is inevitably contaminated by noise. To eliminate the negative affects of noise, this paper introduces region covariance descriptor (R-CD) to characterize the HSI data and proposes a robust patch tensor-based multigraph embedding (RPTMGE) method for DR of HSI. RPTMGE constructs three types of subgraphs to comprehensively describe the intrinsic structure of HSI. Specifically, the manifold subgraph in RPTMGE is constructed with the RCD of HSI, which can significantly enhance the robustness of RPTMGE. Finally, experiments on real HSI data are conducted and the results demonstrated the effectiveness of the proposed method.

Owing to the strong correlation between the spectral bands of hyperspectral images (HSIs), many feature extraction (FE) methods have been proposed to reduce the redundancy of hyperspectral data. However, Euclidean distance-based FE methods are sensitive to noise. To address this issue, this letter proposed a new unsupervised FE method called robust projection learning (RPL) by integrating the low-rank and sparse decomposition with projection learning. Specifically, in order to enhance the discrimination of traditional robust principal component analysis (RPCA), discriminative RPCA (DRPCA) is first proposed by decomposing the raw data into a low-rank part, a discriminative sparse part, and a structured noise. Moreover, for the purpose of redundancy reduction, projection learning is integrated into DRPCA to obtain a projection matrix with robustness and discrimination. To verify the validity of RPL, two real hyperspectral data sets are used for basic comparison and robust analysis. The corresponding experimental results demonstrate that RPL outperforms the comparative FE methods.

Recently, deep learning has been widely applied in hyperspectral image (HSI) classification since it can extract high-level spatial-spectral features. However, deep learning methods are restricted due to the lack of sufficient annotated samples. To address this problem, this letter proposes a novel generative adversarial network (GAN) for HSI classification that can generate artificial samples for data augmentation to improve the HSI classification performance with few training samples. In the proposed network, a new discriminator is designed by exploiting capsule network (CapsNet) and convolutional long short-term memory (ConvLSTM), which extracts the low-level features and combines them together with local space sequence information to form the high-level contextual features. In addition, a structured sparse L2,1constraint is imposed on sample generation to control the modes of data being generated and achieve more stable training. The experimental results on two real HSI data sets show that the proposed method can obtain better classification performance than the several state-of-the-art deep classification methods.

Graph-based dimensionality reduction (DR) techniques are of great interest in the field of image processing and especially on the analysis of hyperspectral images (HSIs). Considering the characteristics of hyperspectral data, many different types of graphs were designed to describe the structure of HSIs. Generally, the algorithms based on these graphs achieved promising performance. However, most of them only focus on how to improve the measurement of similarity between the data points by a single graph. Specifically, vector-based graph methods fail to capture the spatial information, while tensor-based graph methods assume that the pixels in each patch tensor belong to the same class, which is not exactly correct in practice. To overcome these shortcomings, this article proposes a patch tensor-based multigraph embedding (PTMGE) framework for the DR of HSIs, in which three different types of subgraphs are constructed to comprehensively describe the intrinsic geometrical structures of HSIs. First, a tensor subgraph is constructed to capture the spatial information and local geometrical structure. Second, for each two neighboring patch tensors in the tensor graph, a bipartite graph is designed to characterize the pixel-based relationships between the patch tensors. Then, considering that the diversity of pixels may be existed in each patch tensor, a pixel-based subgraph is built to describe the inner geometrical structures of every patch tensor. Finally, a novel graph fusion strategy is designed to calculate a final similarity matrix for projection learning. Experiments on three real hyperspectral data sets are conducted, and comparison with some state-of-the-art algorithms validated the effectiveness of our proposed PTMGE method.

By considering the cubic nature of hyperspectral image (HSI) to address the issue of the curse of dimensionality, we have introduced a tensor locality preserving projection (TLPP) algorithm for HSI dimensionality reduction and classification. The TLPP algorithm reveals the local structure of the original data through constructing an adjacency graph. However, the hyperspectral data are often susceptible to noise, which may lead to inaccurate graph construction. To resolve this issue, we propose a modified TLPP (MTLPP) via building an adjacency graph on a dual feature space rather than the original space. To this end, the region covariance descriptor is exploited to characterize a region of interest around each hyperspectral pixel. The resulting covariances are the symmetric positive definite matrices lying on a Riemannian manifold such that the Log-Euclidean metric is utilized as the similarity measure for the search of the nearest neighbors. Since the defined covariance feature is more robust against noise, the constructed graph can preserve the intrinsic geometric structure of data and enhance the discriminative ability of features in the low-dimensional space. The experimental results on two real HSI data sets validate the effectiveness of our proposed MTLPP method.

Recently, low-rank embedding (LRE) has yielded satisfactory results in dimensionality reduction (DR), for which low-rank representation and projection learning are integrated into one model to generate robust low-dimensional features. However, LRE requires to convert samples into vectors even if the data naturally appear in high-order form. Furthermore, LRE fails to take the label information into consideration. To address these problems, this paper proposes a novel supervised DR method based on multilinear algebra, i.e., the algebra of tensors. By the motivation of extending LRE into tensor space and simultaneously combining the tensor discriminant analysis, we establish tensor low-rank discriminant embedding (TLRDE) model for hyperspectral image (HSI) DR. The model of TLRDE is solved by an alternative iteration algorithm, whose convergence is also mathematically proven. The proposed TLRDE method employs the tensor representation to preserve the intrinsic geometrical structure, uses low-rank reconstruction to uncover the potential relationship among the data points, and combines label information to enhance the discriminability of features. Moreover, the proposed TLRDE does not suffer from the small sample size problem. The experimental results on three real HSI data sets validate the effectiveness of our proposed TLRDE method.

By considering the cubic nature of hyperspectral image (HSI) and to address the issue of the curse of dimensionality, we introduce a tensor locality preserving projection (TLPP) algorithm for HSI classification. TLPP has been proved to be effective in preserving the geometrical structure of data for dimensionality reduction. More importantly, data can be taken directly in the form of a tensor of arbitrary order as input, such that the damage to sample's geometrical structure is avoided during vectorizing. For the HSI classification, TLPP can effectively embed both spatial structure and spectral information into low-dimensional space simultaneously by a series of projection matrices trained for each mode of input samples. The experimental results on the AVIRIS hyperspectral image confirm the effectiveness of TLPP.