The objective of hyperspectral remote sensing image salient object detection (HRSI-SOD) is to identify objects or regions that exhibit distinct spectrum contrasts with the background. This area holds significant promise for practical applications; however, progress has been limited by a notable scarcity of dedicated datasets and methodologies. To bridge this gap and stimulate further research, we introduce the first HRSI-SOD dataset, termed HRSSD, which includes 704 hyperspectral images and 5327 pixel-level annotated salient objects. The HRSSD dataset poses substantial challenges for salient object detection algorithms due to large scale variation, diverse foreground-background relations, and multi-salient objects. Additionally, we propose an innovative and efficient baseline model for HRSISOD, termed the Deep Spectral Saliency Network (DSSN). The core of DSSN is the Cross-level Saliency Assessment Block, which performs pixel-wise attention and evaluates the contributions of multi-scale similarity maps at each spatial location, effectively reducing erroneous responses in cluttered regions and emphasizes salient regions across scales. Additionally, the High-resolution Fusion Module combines bottom-up fusion strategy and learned spatial upsampling to leverage the strengths of multi-scale saliency maps, ensuring accurate localization of small objects. Experiments on the HRSSD dataset robustly validate the superiority of DSSN, underscoring the critical need for specialized datasets and methodologies in this domain. Further evaluations on the HSOD-BIT and HS-SOD datasets demonstrate the generalizability of the proposed method. The dataset and source code are publicly available at https://github.com/laprf/HRSSD.

Discriminative correlation filter (DCF)-based methods have demonstrated superior performance in UAV tracking via fusing multiple types of features and updating models online. However, most DCF-based trackers simply cascade different features, failing to fully take advantage of their complementary strength. In addition, online update strategies are limited to using a single and fixed learning rate, which often leads to model degradation when suffering tracking challenges. In this paper, we present an Auto-Perceiving Correlation Filter (APCF) which explicitly models the target and context with a novel Target State and Background Perception (TSBP) feature. Concretely, we first propose a simple yet effective State Evaluation Metric (SEM) to estimate target states by analyzing the spatial distribution of responses. Based on SEM, we extract TSBP features by adaptively selecting effective features depending on the current target state. Accordingly, a new online model update strategy is also introduced to avoid model degradation. Moreover, we further introduce a perception regularization term to make the extracted feature emphasis more on the target rather than background. Extensive experiments on four widely-used UAV benchmarks have well demonstrated the superiority of the proposed method compared with both DCF and deep learning based trackers while running at a high speed of 76.7 FPS on a single CPU. In addition, APCF with deep features also performs favorably against state-of-the-art trackers.

Compared with remote sensing image (RSI) captioning methods based on the traditional encoder-decoder model, two-stage RSI captioning methods include an auxiliary remote sensing task to provide prior information, which enables them to generate more accurate descriptions. In previous two-stage RSI captioning methods, however, the image captioning and the auxiliary remote sensing tasks are handled separately, which is time-consuming and ignores mutual interference between tasks. To solve this problem, we propose a novel joint-training two-stage (JTTS) RSI captioning method. We use multi-label classification to provide prior information, and we design a differentiable sampling operator to replace the traditional non-differentiable sampling operation to index the multi-label classification result. In contrast to previous two-stage RSI captioning methods, our method can implement joint-training, and the joint loss allows the error of the generated description to flow into the optimization of the multi-label classification via back-propagation. Specifically, we approximate the Heaviside step function with the steep logistic function to implement a differentiable sampling operator for the multi-label classification. We propose a dynamic contrast loss function for multi-label classification task to ensure that a certain margin is maintained between the probabilities of the positive label and the negative label during sampling. We design an attribute-guided decoder to filter the multi-label prior information obtained by the sampling operator to generate more accurate image captions. The results of extensive experiments show that the JTTS method achieves state-of-the-art performance on the RSICD, the UCM-Captions, and the Sydney-Captions datasets.

Authentication is the first entrance to kinds of information systems; however, traditional centered single-side authentication is weak and fragile, which has security risk of single-side failure or breakdown caused by outside attacks or internal cheating. In the edge and Internet-of-Things (IoT) environment, blockchain can apply edge devices to better serve the IoT and provide decentralized high security service solutions. In this article, we proposed a blockchain-based decentralized authentication modeling scheme (named BlockAuth) in edge and IoT environment to provide a more secure, reliable, and strong fault tolerance novel solution, in which each edge device is regarded as a node to form a blockchain network. We designed secure registration and authentication strategy, blockchain-based decentralized authentication protocol, and developed the blockchain consensus, smart contract, and implemented a whole blockchain-based authentication platform for the feasibility, security, and performance evaluation. The analysis and evaluation show that the proposed BlockAuth scheme provides a more secure, reliable, and strong fault tolerance decentralized novel authentication with high-level security driven configuration management. The proposed BlockAuth scheme is suitable for password-based, certificate-based, biotechnology-based, and token-based authentication for high-level security requirement system in edge and IoT environment.

The sixth-generation (6G) wireless technology contributes to the establishment of the Internet of Things (IoT). Recently, the IoT has become popular because of its smart architectures and various applications. Among these applications, intelligent urban surveillance systems for smart cities are becoming more and more important. Therefore, designing a robust visual tracking method has become an urgent task. Deep Siamese convolutional neural networks have been applied to visual tracking recently because of their advantageous abilities to learn a matching function between the template and the target candidate. Unlike traditional Siamese networks, which separately treat the two branches, we propose deep Siamese cross-residual learning to entangle the two branches from the beginning to the end of the Siamese network. This strategy can make the two branches exchange instance-specific information at different nodes of the network and learn a more compact representation of the target. In addition, we propose a combined loss function, which consists of two complementary tasks. One task is to learn a matching function directly and the other one is to learn a classification function. Moreover, our model does not need to load any pretrained weights and is trained with limited sequences from scratch. Plenty of experiments show that our tracker performs favorably against many state-of-the-art tracking methods.