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Abstract Background Currently, obesity has been recognized to be an independent risk factor for osteoarthritis (OA), and the Metabolic Score for Visceral Fat (METS-VF) has been suggested to be potentially more accurate than body mass index (BMI) in the assessment of obesity. Nevertheless, the correlation of METS-VF with OA has not been obviously revealed yet. Therefore, this study aimed to delve into the potential relationship between METS-VF and OA. Methods By examining data from the NHANES (2009–2018), weighted multivariate logistic regression analyses were used for assessing the correlation between METS-VF and OA. Subgroup analyses were then performed to validate the findings. Moreover, the nonlinear relationship between the two was assessed by restricted cubic spline (RCS). Receiver operating characteristic (ROC) curves were plotted to examine the diagnostic accuracy of METS-VF versus previous obesity index for OA. Results This study involved 7639 participants. According to our results, METS-VF was notably related to an elevated risk of OA, regardless of the METS-VF and the trend of positive association was more pronounced with the elevating METS-VF level (p for trend

Abstract Objective Chronic kidney disease (CKD) and osteoarthritis (OA) represent two frequently seen disorders among the general population, and they share several similar risk factors. The present work focused on assessing the relation of CKD with OA. Methods This cohort study included 26,280 eligible participants aged ≥ 20 years who had valid data on CKD and OA from the National Health and Nutrition Examination Survey (NHANES) 2011–2020. The association between CKD and OA was studied by logistic regression, adjusting for demographics, body mass index (BMI), socioeconomic factors, physical activity, ever smoking, alcohol using, diabetes status and hypertension status. Results Among the participants of this study, 26.69% of OA patients had concurrent CKD, whereas this proportion was only 13.83% among non-OA patients.CKD was related to OA[OR:2.269 (95%CI:2.266–2.271), p

In recent years, the wide use of deep learning based methods has greatly improved the classification performance of hyperspectral image (HSI). As an effective method to improve the performance of deep convolution networks, attention mechanism is also widely used for HSI classification tasks. However, the majority of the existing attention mechanisms for HSI classification are based on the convolution layer, and the classification accuracy still has margins for improvement. Motivated by the latest self attention mechanism in natural language processing, a deep transformer is proposed for HSI classification in this paper. Specifically, deep transformer along the spectral dimension and the spatial dimension are explored respectively. Then, a deep spatial-spectral transformer (DSS-TRM) is proposed to improve the classification performance of HSI. The contribution of this paper is to make full use of self attention mechanism, that is to use transformer layer instead of convolution layer. More importantly, a DSS-TRM is proposed to realize end-to-end HSI classification. Extensive experiments are conducted on three HSI data sets. The experimental results demonstrates that the proposed DSS-TRM could outperform the traditional convolutional neural networks and attention based methods.

Abstract To address the existing problems of capsule networks in deep feature extraction and spatial‐spectral feature fusion of hyperspectral images, this paper proposes a hyperspectral image classification method that combines a deep residual 3D capsule network and Markov random field. Based on this method, the deep spatial‐spectral features of hyperspectral images are extracted using the deep residual 3D convolutional structure, the vector capsules of the features are obtained by the initial capsule layer and mapped into probability capsules via the 3D dynamic routing mechanism to construct the classification probability map, and the spatial structure of the classification results is regularised by the Markov random field to further improve the classification accuracy and performance of the images. Two sets of benchmark hyperspectral images, namely Indian Pines and Pavia University data sets, were used to conduct comparative experiments and ablation study. The experimental results showed that, compared with the conventional convolutional neural network and existing capsule network models, the proposed method not only improves the classification accuracy of the images but also partly eliminates the category noise and affords a more regular classification probability map.

Recently, unsupervised domain adaptation (UDA) segmentation of remote sensing images (RSIs) has attracted a lot of attention. However, the performance of such methods still lags far behind that of their supervised counterparts. To this end, this paper focuses on a more practical yet under-investigated problem, semi-supervised domain adaptation (SSDA) segmentation of RSIs, to effectively improve the segmentation results of targeted RSIs with a few labeled samples. First, differently from the existing single-prototype mode, a novel cross-domain multi-prototype constraint is proposed, to deal with large inter-domain discrepancies and intra-domain variations. Specifically, each class is represented as a set of prototypes, so that multiple sets of prototypes corresponding to different classes can better model complex inter-class differences, while different prototypes within the same class can better describe the rich intra-class relations. Meanwhile, the multi-prototypes are calculated and updated jointly using source and target samples, which can effectively promote the utilization and fusion of the feature information in different domains. Second, a contradictory structure learning mechanism is designed to further improve the domain alignment, with an enveloping form. Third, self-supervised learning is adopted, to increase the number of target samples involved in prototype updating and domain adaptation training. Extensive experiments verified the effectiveness of the proposed method for two aspects: (1) Compared with the existing SSDA methods, the proposed method could effectively improve the segmentation performance by at least 7.38%, 4.80%, and 2.33% on the Vaihingen, Potsdam, and Urban datasets, respectively; (2) with only five labeled target samples available, the proposed method could significantly narrow the gap with its supervised counterparts, which was reduced to at least 4.04%, 6.04%, and 2.41% for the three RSIs.

The deep learning method has achieved great success in hyperspectral image classification, but the lack of labeled training samples still restricts the development and application of deep learning methods. In order to deal with the problem of small samples in hyperspectral image classification, a novel small sample classification method based on rotation-invariant uniform local binary pattern (RULBP) features and a graph-based masked autoencoder is proposed in this paper. Firstly, the RULBP features of hyperspectral images are extracted, and then the k-nearest neighbor method is utilized to construct the graph. Furthermore, self-supervised learning is conducted on the constructed graph so that the model can learn to extract features more suitable for small sample classification. Since the self-supervised training mainly adopts the masked autoencoder method, only unlabeled samples are needed to complete the training. After training, only a small number of samples are used to fine-tune the graph convolutional network, so as to complete the classification of all nodes in the graph. A large number of classification experiments on three commonly used hyperspectral image datasets show that the proposed method could achieve higher classification accuracy with fewer labeled samples.

Deep learning based methods have recently been successfully explored in hyperspectral image classification field. However, training a deep learning model still requires a large number of labeled samples, which is usually impractical in hyperspectral images. In this paper, a simple but effective feature extraction method is proposed for hyperspectral image classification. Specifically, a pretrained deep convolutional neural network based on the ImageNet dataset is used to extract spatial features of a hyperspectral image. Recently, it is easy to obtain a pretrained convolutional neural network on the Internet. Note that the pretrained models are trained by using the ImageNet dataset. This means that the proposed method does not need labeled hyperspectral samples to train the deep model. Therefore, the proposed method alleviates the problem of lacking labeled samples and avoids the artificial design of feature extraction rules. Finally, the extracted features are stacked with spectral features as the input of a support vector machine classifier. The proposed method is conducted on three widely used hyperspectral image datasets. The experimental results demonstrate that the proposed method could outperform the conventional feature extraction methods and deep learning based methods.

Deep learning based methods have made great progress in hyperspectral image classification. However, training a deep learning model often requires a large number of labeled samples, which are not always available in practical applications. In this paper, a simple but innovative classification paradigm to exploit morphological attribute profile cube is proposed to improve the small sample classification performance of hyperspectral image. First, morphological attribute profiles are constructed by applying different morphological filters to hyperspectral image. Morphological attribute profile cubes are then extracted as the feature of a sample. Second, the obtained morphological attribute profile cubes are scanned with multiple scale sliding windows to make full use of the rich spatial-spectral information. Finally, the features after multi-grained scanning are input into a deep forest classifier to complete the classification task. In this way, the proposed method could use a deep network structure to improve the classification accuracy. To demonstrate the effectiveness of the proposed method, the classification experiments are carried on three widely used hyperspectral data sets. The experimental results demonstrate that the proposed method can outperform the conventional semi-supervised methods and the state-of-the-art deep learning based methods. The demo code on the Salinas dataset is released on the page: https://github.com/liubing220524/ MAPC-DRF-HSI.

Recently, the deep learning models have achieved great success in hyperspectral images (HSI) classification. However, most of the deep learning models fail to obtain satisfactory results under the condition of small samples due to the contradiction between the large parameter space of the deep learning models and the insufficient labeled samples in HSI. To address the problem, a deep model based on the induction network is designed in this article to improve the classification performance of HSI under the condition of small samples. Specifically, the typical meta-training strategy is adopted, enabling the model to acquire stronger generalization ability, so as to accurately distinguish the new classes with only a few labeled samples (e.g., five samples per class). Moreover, in order to deal with the disturbance caused by the various characteristics of the samples in the same class in HSI, the class-wise induction module is introduced utilizing the dynamic routing algorithm, which can induce the sample-wise representations to the class-wise level representations. The obtained class-wise level representations possess better separability, allowing the designed model to generate more accurate and robust classification results. Extensive experiments are carried out on three public HSI to verify the effectiveness of the proposed method. The results demonstrate that our method outperforms existing deep learning methods under the condition of small samples.

Cross-domain classification with small samples is a more challenging and realistic experimental setup. Until now, few studies have focused on the problem of small-sample cross-domain classification between completely different hyperspectral images (HSIs) since they possess different land cover types and statistical characteristics. To this end, this paper proposes a general-purpose representation learning method for cross-domain HSI classification, aiming to enable the model to learn more general-purpose deep representations that can quickly adapt to different target domains with small samples. The core of this method is to propose a novel three-level distillation strategy to transfer knowledge from multiple models well-trained on source HSIs into a single distilled model at the channel-, feature- and logit-level simultaneously. The learned representations can be further fine-tuned with small samples and quickly adapt to new target HSIs and previously unseen classes. Specifically, to transfer and fuse knowledge from multiple-source domains into a single model simultaneously and solve the inconsistency of the number of bands in different HSIs, an extensible multi-task model, including the channel transformation module, the feature extraction module and the linear classification module, is designed. Only the feature extraction module is shared across different HSIs, while the other two modules are domain-specific. Furthermore, the typical episode-based learning strategy of the metric-based meta-learning is adopted in the whole learning process to further improve the generalization ability and data efficiency. Extensive experiments are conducted on six source HSIs and four target HSIs, and the results demonstrate that the proposed method outperforms the existing advanced methods in cross-domain HSI classification with small samples.

Hyperspectral image classification methods based on deep learning have led to remarkable achievements in recent years. However, these methods with outstanding performance are also accompanied by problems such as excessive dependence on the number of samples, poor model generalization, and time-consuming training. Additionally, the previous patch-level feature extraction methods have some limitations, for instance, non-local information is difficult to model, etc. To solve these problems, this paper proposes an image-level feature extraction method with transfer learning. Firstly, we look at a hyperspectral image with hundreds of contiguous spectral bands from a sequential image perspective. We attempt to extract the global spectral variation information between adjacent spectral bands by using the optical flow estimation method. Secondly, we propose an innovative data adaptation strategy to bridge the gap between hyperspectral and video data, and transfer the optical flow estimation network pre-trained with video data to the hyperspectral feature extraction task for the first time. Thirdly, we utilize the traditional classifier to achieve classification. Simultaneously, a vote strategy combined with features at different scales is proposed to improve the classification accuracy further. Extensive, well-designed experiments on four scenes of public hyperspectral images demonstrate that the proposed method (Spe-TL) can obtain results that are competitive with advanced deep learning methods under various sample conditions, with better time effectiveness to adapt to new target tasks. Moreover, it can produce more detailed classification maps that subtly reflect the authentic distribution of ground objects in the original image.

Classification with a few labeled samples has always been a longstanding problem in the field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample characteristics of HSI classification, a novel ensemble self-supervised feature-learning (ES2FL) method is proposed in this paper. The proposed method can automatically learn deep features conducive to classification without any annotation information, significantly reducing the dependence of deep-learning models on massive labeled samples. Firstly, to utilize the spatial–spectral information in HSIs more fully and effectively, EfficientNet-B0 is introduced and used as the backbone to model input samples. Then, through constraining the cross-correlation matrix of different distortions of the same sample to the identity matrix, the designed model can extract the latent features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. In addition, two ensemble learning strategies, feature-level and view-level ensemble, are proposed to further improve the feature-learning ability and classification performance by jointly utilizing spatial contextual information at different scales and feature information at different bands. Finally, the concatenations of the learned features and the original spectral vectors are inputted into classifiers such as random forest or support vector machine to complete label prediction. Extensive experiments on three widely used HSI data sets show that the proposed ES2FL method can learn more discriminant deep features and achieve better classification performance than existing advanced methods in the case of small samples.

Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial−spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models.