The emergence of malicious nodes in blockchain networks poses a serious threat to the integrity and security of these systems. Malicious activities can disrupt the consensus process and compromise the overall security of the network. Although traditional consensus mechanisms have proven effective to some extent, they often struggle to tolerate many malicious nodes, which can lead to network instability or even failure. In response to these challenges, this paper presents a novel blockchain sharding consensus algorithm designed to withstand an unlimited scale of malicious nodes (USMN-SCA). Our approach leverages a dynamic credit mechanism to evaluate node behavior and categorize nodes into different credit levels. By dividing the blockchain network into credit-based shards and isolating low-credit nodes, we mitigate the impact of malicious activities and improve the network’s resilience. USMN-SCA employs a two-stage consensus process: first, within each shard, and then between shards. This ensures that consensus is reached efficiently while maintaining a high level of security. We implement the USMN-SCA algorithm in a prototype via blockchain sharding and deploy it on the Alibaba Cloud. The experimental results show that our protocol significantly outperforms existing methods in terms of security, with a success probability of 100% in a real-world Web environment, even more then 50% of the nodes are malicious. Additionally, the consensus delay and throughput performance are comparable to those of other state-of-the-art consensus algorithms. These findings establish the USMN-SCA as a cutting-edge solution with high applicability, scalability, and compatibility across various blockchain platforms. This work represents a significant advancement in blockchain security and performance optimization, paving the way for more secure and efficient blockchain applications.

Pansharpening is a spatial-spectral fusion technique that fuses low-resolution multispectral (MS) images with high-resolution panchromatic (PAN) images to get high-resolution multispectral images which are rich in spectral and spatial information. Some pansharpening methods based on dynamic convolution were proposed to improve the adaptivity and generalization of fusion network. However, these methods either only focus on local small regions or generate dynamic filters with a complex network. Besides, the dynamic filters in the existing methods only convolve with MS or PAN image, resulting in that the extracted details or spectral features are inadequate. In this article, we propose a dynamic large-small kernel convolutional network. To obtain small scale features, we propose a dual dynamic small kernel (DDSK) module which consists of dynamic spatial small filter (DASF) and dynamic spectral small filter (DESF). The multiscale dynamic large kernel convolution (MDLC) module is designed to expand the receptive field for obtaining large scale features. Considering the differences between PAN and MS images, DASF and spatial MDLC modules are presented to extract the details of PAN image. Similarly, DESF and spectral MDLC modules are proposed to obtain the spectral features of MS images. The proposed method is evaluated on GaoFen-2 and WorldView-3 datasets, and our method shows good performance.

Pansharpening has benefited from the development of deep learning (DL) and has achieved excellent results. However, most DL-based methods extract local features by convolutional neural networks and do not integrate global features. Moreover, these methods only extract high-frequency features on the high-pass domain (HPD) or only consider image features on the intensity domain (ID). The method that only considers features in one domain may result in insufficient extraction of spatial and spectral features. Therefore, we propose a dynamic local–global network model on dual-domains, that is, HPD and ID. The dynamic local–global feature extraction block (DLGB) is designed to dynamically integrate local and global features to improve the representation capability of the network. To decrease the computational complexity of global feature extraction, a lightweight biaxial nonlocal attention (BNLA) that captures global spatial features in horizontal and vertical directions is proposed. Experiments on GeoEye-1, QuickBird, and WorldView-3 datasets show that the proposed method presents better fusion performance on objective evaluation indices and subjective perception.

Nowadays, high-resolution images with rich spectral information are necessary for earth observation. Remote-sensing image fusion is an effective method to provide high-resolution multispectral images, which are obtained by fusing high-resolution panchromatic images and low-resolution multispectral images. However, existing methods mostly use the same network for image feature extraction, without considering the differences among different pixels, resulting in that the extracted features are not accurate enough. This letter proposes a spatial dynamic selection network for remote-sensing image fusion. A dynamic feature extraction module composed of multiple spatial dynamic blocks (SDBs) and cross-scale context connection blocks (CSCBs) is designed. The SDB can extract image features according to the input by different networks, and realize dynamic selection of pixel features. Since the spatial structure and spectral characteristic of each pixel are different, two complementary branches are designed in the SDB to extract different features, which improves the capability of feature extraction. Multiscale network structure is designed to obtain more abundant information and the CSCB is used to integrate the information of different scales. Experimental results on GeoEye-1 and WorldView-3 datasets demonstrate the superiority of the proposed method.

Hyperspectral images (HSIs) contain rich spectral information and have great application value. However, due to various hardware limitations, the spatial resolution of HSIs acquired by the sensor is low. HSI super-resolution (SR) attracts much attention to improve spatial quality. In this letter, a single HSI SR method based on network fusion is proposed. Our method includes the SR network part and fusion part. In the SR network part, we construct 3-D multiscale mixed attention networks (3-D-MSMANs) by cascading 3-D multiscale mixed attention block (3-D-MSMAB) to restore high-resolution HSIs. 3-D-MSMAB consists of the 3-D Res2net module and the mixed attention module. 3-D Res2net module is a simple and effective multiscale method. The mixed attention module is proposed by combining the first- and second-order statistics of features. In addition, we use the mutual learning loss between 3-D-MSMAN so that they can learn from each other. In the fusion part, the fusion module is designed to merge the output of each 3-D-MSMAN. Our method can achieve good results in both simulated and real SR experiments. Code is available athttps://github.com/LYT-max/Mixed-Attention-for-HSI-SR.

Deep convolutional neural networks (CNNs) have made great progress in the super-resolution (SR) of hyperspectral images (HSIs). However, most methods utilize convolution to explore local features, and global features are ignored. It is expected that combining non-local mechanism with CNN will improve the performance of HSI SR. This paper presents a multi-level progressive HSI SR network. The dense non-local and local block (DNLB) is constructed to combine local and global features, which are used to reconstruct super-resolution images at each level. Due to the high dimension of HSI, original non-local methods produce memory-expensive attention maps. We develop a non-local channel attention block to extract the global features of HSIs efficiently. Spatial-spectral gradient is injected in the non-local attention block to obtain better details. Furthermore, the progressive learning mode based multi-level network is proposed to reconstruct HSI with fine details. A number of experiments demonstrate that our method can reconstruct hyperspectral images more accurately than existing methods.

Due to rich spectral information, hyperspectral images (HSIs) have been widely used in various fields. However, limited by imaging systems, the low spatial resolution of HSIs has become an important problem. In this article, for enhancing the spatial resolution, Interactformer is proposed to interact with global and local features extracted by Transformer and 3D convolutional neural network (CNN) branches. Within the Transformer branch, a separable self-attention module with linear complexity is designed to solve the problem that traditional self-attention mechanisms suffer from large memory costs due to quadratic complexity. In the 3D CNN branch, the spectral attention module and 3D convolution are applied jointly to better protect the spectral correlation among spectral bands and facilitate local feature extraction of HSIs. The interactive attention unit between the two parallel branches is designed to interact with local and global feature information adaptively. Compared with state-of-the-art super-resolution (SR) methods, the proposed method reconstructs better HSI in simulated SR experiments, real SR experiments, and classification experiments, which prove that Interactformer can effectively improve the spatial resolution while preserving the spectral information.

Coastal wetland monitoring plays an important role in the protection and restoration of ecosystems in this world. UAV-hyperspectral imaging, as an emerging technique for Earth observation and space exploration, provides the huge potential ability to identify different wetland species. In this work, a multilayer global spectral–spatial attention network (MGSSAN) is proposed for mapping coastal wetlands, which mainly consists of two major steps. First, a two-branch convolutional neural network (CNN) framework with residual connection is developed to obtain an initial classification probability map, in which one branch is used to capture the spectral information, the other branch is used to extract spatial information, and a global spectral–spatial attention module is designed to guide networks focusing on those features that are more discriminative. Second, an extended random walker method is utilized to optimize the initial classification probabilities, so as to yield the final map. Experiments performed on three wetland HSI datasets created by ourselves verify that the proposed method can obtain superior performance with respect to several state-of-the-art hyperspectral image classification methods.

Pan-sharpening is an effective method to obtain high-resolution multispectral images by fusing panchromatic (PAN) images with fine spatial structure and low-resolution multispectral images with rich spectral information. In this article, a multiscale pan-sharpening method based on dynamic convolutional neural network is proposed. The filters in dynamic convolution are generated dynamically and locally by the filter generation network which is different from the standard convolution and strengthens the adaptivity of the network. The dynamic filters are adaptively changed according to the input images. The proposed multiscale dynamic convolutions extract detail feature of PAN image at different scales. Multiscale network structure is beneficial to obtain effective detail features. The weights obtained by the weight generation network are used to adjust the relationship among the detail features in each scale. The GeoEye-1, QuickBird, and WorldView-3 data are used to evaluate the performance of the proposed method. Compared with the widely used state-of-the-art pan-sharpening approaches, the experimental results demonstrate the superiority of the proposed method in terms of both objective quality indexes and visual performance.

A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a two-scale decomposition of an image into a base layer containing large scale variations in intensity, and a detail layer capturing small scale details. A novel guided filtering-based weighted average technique is proposed to make full use of spatial consistency for fusion of the base and detail layers. Experimental results demonstrate that the proposed method can obtain state-of-the-art performance for fusion of multispectral, multifocus, multimodal, and multiexposure images.

In this paper we introduce the nonsubsampled shear let transform for multi-focus image fusion. In the proposed method, source images are decomposed by nonsubsampled shear let transform firstly. Then the decomposition coefficients are merged according to the given fusion rule. Finally the fused image is reconstructed by inverse nonsubsampled shear let transform. The experimental results over five pairs of registered multi-focus images and one pair of mis-registered multi-focus images demonstrate the superiority of the proposed method.

In this paper, we propose a novel feature vector clustering method for unsupervised change detection in multitemporal satellite images. A feature vector for each pixel is extracted using the compressed sparse representation of the difference image which is obtained by comparing a pair of co-registered images acquired at different times on the same area. The compressed sparse representation is achieved by taking two stages: compressed sampling and sparse representation. The compressed sampling is first employed in order to reduce the dimensionality of the feature vectors. Then, the sparse representation is applied to extract the meaningful change information and to combat the noise interference. The final change detection is obtained by clustering the extracted feature vectors using k-means algorithm into “changed” and “unchanged” classes. Experimental results clearly show that the proposed approach consistently yields superior performance compared to several well-known change detection techniques on both noise-free and noisy satellite images.

This paper presents a novel method based on the developed multiscale dual bilateral filter to fuse high spatial resolution panchromatic image and high spectral resolution multispectral image. Compared with traditional multi-resolution based methods, the process of detail extraction considers the characteristics of panchromatic image and multispectral image simultaneously. The low resolution multispectral image is resampled to the same size of the high resolution panchromatic image and sharpened through injecting the extracted details. The proposed fusion method is tested over QuickBird and IKONOS images and compared with three popular methods. The experimental results demonstrate that our method outperforms conventional methods.

Image super resolution is a challenging highly ill-posed inverse problem. In this paper, we proposed a texture constrained sparse representation for single image super resolution. Firstly, the low resolution observed image is segmented into different texture regions. Through preprepared texture databases, the low resolution regions are classified into different texture categories using the designed texture classifier. Then, the high resolution segments are reconstructed by sparse representation with relevant texture dictionaries. Integrating all segments, the high resolution result is obtained. The proposed method is compared with sparse representation method and some existing methods. The experimental results show that our method achieves better results in visual inspection and quantitative analysis.

This paper presents the monotonic indices space (MIS) method used for the extended complex system capability indices effectiveness analysis. Based on the assumption that indices are monotonic with respect to the requirement measurements, an algorithm is proposed and applied to attain numerical approximation of monotonic indices requirement locus with hyperboxes. Two algorithms for acquiring intersection of several monotonic indices requirement loci are proposed, and two system analysis models based on MIS, the system evaluation model and the index sensitivity analysis model, are put forward. Finally, the models previously mentioned are used to analyze the capability indices effectiveness of a notional antistealth information system. The results show that the MIS method is promising.