Restricted by technical and budget constraints, hyperspectral (HS) image which contains abundant spectral information generally has low spatial resolution. Fusion of hyperspectral and panchromatic (PAN) images can merge spectral information of the former and spatial information of the latter. In this paper, a new hyperspectral image fusion algorithm using structure tensor is proposed. An image enhancement approach is utilized to sharpen the spatial information of the PAN image, and the spatial details of the HS image is obtained by an adaptive weighted method. Since structure tensor represents structure and spatial information, a structure tensor is introduced to extract spatial details of the enhanced PAN image. Seeing that the HS and PAN images contain different and complementary spatial information for a same scene, a weighted fusion method is presented to integrate the extracted spatial information of the two images. To avoid artifacts at the boundaries, a guided filter is applied to the integrated spatial information image. The injection matrix is finally constructed to reduce spectral and spatial distortion, and the fused image is generated by injecting the complete spatial information. Comparative analyses validate the proposed method outperforms the state-of-art fusion methods, and provides more spatial details while preserving the spectral information.

A novel local structure descriptor (LSD) for color image retrieval is proposed in this paper. Local structures are defined based on a similarity of edge orientation, and LSD is constructed using the underlying colors in local structures with similar edge direction. LSD can effectively combine color, texture and shape as a whole for image retrieval. LSH integrates the advantages of both statistical and structural texture description methods, and it possesses high indexing capability and low dimensionality. In addition, the proposed feature extraction algorithm does not need to train on a large scale training datasets, and it can extract local structure histogram based on LSD. The experimental results on the Corel image databases show that the descriptor has a better image retrieval performance than other descriptors.

Compared to natural images, remote sensing images have the characteristics of high spatial resolution, large target scale variation, dense target distribution, and complex background. Consequently, there are challenges with insufficient detection accuracy and the inability to identify target locations accurately. Therefore, this paper introduces the YOLOX-CA algorithm, based on the YOLOX model, to address these challenges in remote sensing object detection. Firstly, the YOLOX-CA algorithm optimizes the feature extraction network of the YOLOX model. This optimization employs large-kernel depthwise separable convolution in the backbone network to enhance feature extraction capabilities, comprehensively and accurately capturing information features. Secondly, the ACmix attention mechanism is introduced into the backbone network to identify crucial features, enhance feature extraction capability, and expedite network convergence. Lastly, a Contextual Feature Enhancement (CFE) module is constructed and employed in the upsampling process of feature fusion, aiming to augment the model’s awareness of context. Experimental results on the large-scale DIOR dataset for remote sensing object detection demonstrate performance enhancements over the baseline model, with increases of 2.7% in mAP, 1.1% in mAP@0.5, and 2.2% in Recall. The findings from the test dataset suggest that the proposed YOLOX-CA method is applicable and practical for remote sensing object detection, improving detection accuracy while mitigating instances of target omission.

How can we bridge efficiently distribution difference between the source and target domains in an isomorphic latent feature space by using metric learning. This study introduces metric learning on manifolds which combine a cascaded learning network and a metric learning model to form a Unified Domain Adaptation Model. Our approach is based on formulating a transfer from the source to the target as a geometric mean metric learning problem on manifolds. The solution of symmetric positive definite covariance matrices not only reduces the statistical differences between the source and target domains, but also the underlying geometry of the source and target domains using diffusions on the underlying source and target manifolds. By retaining both the nonlinear structure of the Riemannian geometry of the open cone of symmetric positive definite matrices and cascaded learning networks, we improve the state-of-the-art results on the Amazon (A), Caltech256 (C), DSLR (D),Webcam (W) and VisDA benchmark datasets by knowledge transfer, while achieving comparable performances to competing methods on domain adaptation modeling.

In order to tackle some challenges in ubiquitous healthcare monitoring such as mobility, scalability, and network latency, a framework for Fog-assisted healthcare monitoring is proposed in this paper. This framework is composite of body-sensing layer, Fog layer (Fog-assisted gateway), Cloud layer (health Cloud). And then, this paper makes an intensive study in some key technologies of the pro posed framework such as an IPv6-based network architecture, intelligent warning model based on subband energy feature, security framework of HL7 RIM-based data exchange, health risk assessment based on fusion of grey model and Markov model. Finally, results of experiment depict that the proposed intelligent warning model can make immediate distinction abnormal signals. Moreover, the proposed health assessment model confirms its effectiveness with respect to 245 patients in Xiamen District of Jimei.

To address the challenges posed by detection of arbitrary orientation object and small object in remote sensing imagery, this paper proposes an innovative object detection model based on YOLOv8, named YOLOv8-DFS. To improve robustness against arbitrary orientation object, we skillfully design a new model by inserting three SPPELAN modules and a C2f-DCN module into the model. The model not only mines the context of multi-scale and multi-orientation objects through employing weighted fusion of features across different scales, but also enables dynamic adaption of convolution kernel shapes and orientations, thereby significantly enhancing the model’s precision in detecting rotated and inclined targets. To improve the detection performance for small-scale objects, we design IADown and BiConcat modules. The IADown module refines the downsampling strategy, preserving more detailed information and enhancing feature representation for small objects. Concurrently, the BiConcat module facilitates optimized multi-scale feature fusion through a learnable weighted concatenation mechanism, amplifying detailed information necessary of small object recognition. The experimental results on the DIOR-R dataset show that YOLOv8-DFS-s (based on YOLOv8-s) achieves a 2.4% improvement in mAP@0.5 over the baseline, while YOLOv8-DFS-l (based on YOLOv8-l) yields a 1.5% increase. On the HRSC2016 dataset, the results of YOLOv8-DFS-s and YOLOv8-DFS-l show an improvement of 3.1% and 2.0%, respectively. These results validate the efficiency of YOLOv8-DFS in handling both rotational variance and small object detection, demonstrating substantial performance gains across multiple model scales.