Hyperspectral Meets Optical Flow: Spectral Flow Extraction for Hyperspectral Image Classification.

Hyperspectral image (HSI) classification has always been recognised as a difficult task. It is therefore a research hotspot in remote sensing image processing and analysis, and a number of studies have been conducted to better extract spectral and spatial features. This study aimed to track the variation of the spectrum in hyperspectral images from a sequential data perspective to obtain more distinguishable features. Based on the characteristics of optical flow, this study introduces an optical flow technique for the extraction of spectral flow that denotes the spectral variation and implements a dense optical flow extraction method based on deep matching. Lastly, the extracted spectral flow are combined with the original spectral features and input into a commonly used support vector machine (SVM) classifier to complete the classification. Extensive classification experiments on three benchmark HSI test sets show that the classification accuracy obtained by the spectral flow extracted in this study (SpectralFlow) is higher than traditional spatial feature extraction methods, texture feature extraction methods, and the latest deep-learning-based methods. Furthermore, the proposed method can produce finer classification thematic maps, thereby demonstrating strong practical application potential.

Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set Classification.

The difficulties of obtaining sufficient labeled samples have always been one of the factors hindering deep learning models from obtaining high accuracy in hyperspectral image (HSI) classification. To reduce the dependence of deep learning models on training samples, meta learning methods have been introduced, effectively improving the classification accuracy in small sample set scenarios. However, the existing methods based on meta learning still need to construct a labeled source data set with several pre-collected HSIs, and must utilize a large number of labeled samples for meta-training, which is actually time-consuming and labor-intensive. To solve this problem, this paper proposes a novel unsupervised meta learning method with multiview constraints for HSI small sample set classification. Specifically, the proposed method first builds an unlabeled source data set using unlabeled HSIs. Then, multiple spatial-spectral multiview features of each unlabeled sample are generated to construct tasks for unsupervised meta learning. Finally, the designed residual relation network is used for meta-training and small sample set classification based on the voting strategy. Compared with existing supervised meta learning methods for HSI classification, our method can only utilize HSIs without any label for unsupervised meta learning, which significantly reduces the number of requisite labeled samples in the whole classification process. To verify the effectiveness of the proposed method, extensive experiments are carried out on 8 public HSIs in the cross-domain and in-domain classification scenarios. The statistical results demonstrate that, compared with existing supervised meta learning methods and other advanced classification models, the proposed method can achieve competitive or better classification performance in small sample set scenarios.

Deep Hierarchical Vision Transformer for Hyperspectral and LiDAR Data Classification.

In this study, we develop a novel deep hierarchical vision transformer (DHViT) architecture for hyperspectral and light detection and ranging (LiDAR) data joint classification. Current classification methods have limitations in heterogeneous feature representation and information fusion of multi-modality remote sensing data (e.g., hyperspectral and LiDAR data), these shortcomings restrict the collaborative classification accuracy of remote sensing data. The proposed deep hierarchical vision transformer architecture utilizes both the powerful modeling capability of long-range dependencies and strong generalization ability across different domains of the transformer network, which is based exclusively on the self-attention mechanism. Specifically, the spectral sequence transformer is exploited to handle the long-range dependencies along the spectral dimension from hyperspectral images, because all diagnostic spectral bands contribute to the land cover classification. Thereafter, we utilize the spatial hierarchical transformer structure to extract hierarchical spatial features from hyperspectral and LiDAR data, which are also crucial for classification. Furthermore, the cross attention (CA) feature fusion pattern could adaptively and dynamically fuse heterogeneous features from multi-modality data, and this contextual aware fusion mode further improves the collaborative classification performance. Comparative experiments and ablation studies are conducted on three benchmark hyperspectral and LiDAR datasets, and the DHViT model could yield an average overall classification accuracy of 99.58%, 99.55%, and 96.40% on three datasets, respectively, which sufficiently certify the effectiveness and superior performance of the proposed method.

Limited Health Literacy Is Associated With Worse Patient-Reported Outcomes in Inflammatory Bowel Disease.

Background: Patients with inflammatory bowel disease (IBD) face complex health tasks and decisions. Limited health literacy is a risk factor for poor health outcomes, but this has not been examined in IBD. This study aims to assess the role of health literacy for patients with IBD. Methods: We prospectively enrolled adults with IBD receiving care from the Section of Gastroenterology at the Boston Medical Center. In-person, standardized questionnaires were administered to measure health literacy with the Newest Vital Sign, self-efficacy with the Medication Use and Self-Efficacy Scale, quality of life with the 10-question Short Inflammatory Bowel Disease Questionnaire, depression with the Patient-Reported Outcomes Measurement System Short Form, and clinical disease activity for patients with Crohn's disease with the Harvey-Bradshaw Index and for patients with ulcerative colitis with the Simple Clinical Colitis Activity Index (SCCAI). The relationships between health literacy and these variables were subsequently examined. Results: Of 112 patients invited to participate, 99 enrolled and completed the interview. Limited health literacy was identified in 40% (n = 40) of patients. Patients with limited health literacy reported significantly worse overall health (P = 0.03) and more depressive symptoms (P = 0.01). Of the 56 patients with Crohn's disease, those with adequate health literacy were more likely to be in clinical remission (mean Harvey-Bradshaw Index score < 5), compared with those with limited health literacy (odds ratio, 4.15; 95% confidence interval, 1.37 to 13.45; P = 0.01). There was no significant association between health literacy and clinical disease activity (SCCAI) in patients with ulcerative colitis. Conclusions: Limited health literacy is associated with lower ratings of subjective health and depression in IBD and more symptoms of active disease in patients with Crohn's disease.