Event-Triggered Tracking Algorithm for Maritime Vessels Using Remote Sensing and Game Theory

The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as an essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidances on creating benchmark datasets in efficient manners. Following the presented guidances, we also provide an example on building RS image dataset, i.e., Million Aerial Image Dataset (Online. Available: https://captain-whu.github.io/DiRS/0 ), a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this article will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones.

With the great progress of product development technology, product forms have been greatly enriched by cognitive differences; users and designers have formed a “cognitive friction” phenomenon in the product evaluation process, which results in designers being unable to grasp user emotions accurately and risks of product development failure. This paper aims to balance the cognitive differences between cognitive subjects (users and designers) and evaluates the product image form. First, image entropy is used to evaluate and extract the weight of the product target image. Second, fuzzy Theil entropy is used to evaluate the cognitive friction between cognitive subjects, and its existence and size are visually presented. Then, a cognitive friction balance model is built by combining game theory, the comprehensive evaluation weight between cognitive subjects is obtained, and the product image form is ranked and optimized. Finally, all the research steps are described in the form of a household hair dryer. The results show that fuzzy Theil entropy and game theory have significant advantages in the evaluation and balance of cognitive friction in product design. Thus, the cognitive friction evaluation and balance model constructed from the fuzzy Theil entropy and game theory do not only enable different cognitive subjects to achieve cognitive symmetry, but also screen out product forms that meet the cognitive needs of users. This finding provides the theoretical basis and practical significance for the establishment of a closed-loop model in cognitive friction balance and the reduction of cognitive differences between cognitive subjects in the entire process of product design. It also introduces new ways of thinking and methods for cognitive science research.

Remote sensing technologies play a significant role in education as a credible and objective source of information. Since 2017, the National Center “Junior Academy of Sciences of Ukraine” has been supervising the “GIS and Earth Remote Sensing” laboratory, which provides scientific, methodological, and procedural support for the 14 “GIS and Earth Remote Sensing” regional branches’ academic activities. In 2022 a corresponding section has been launched for the ІІІ stage of the nationwide Contest-Presentation of Scientific Research Projects held by the Junior Academy of Sciences of Ukraine. This article examines two approaches to using remote sensing data in education: theoretical (based on current situation with monitoring the consequences of the russian-ukrainian war) and practical (considering the research process and results of two runners-up among the final stage participants of the 2023 JASU Contest-Presentation of Scientific Research Projects in the “GIS and Earth Remote Sensing” section). Remote sensing technologies can be a critical tool for exploring the devastating impact of high scale military actions on the environment and infrastructure. Satellite imagery analysis provides access to the most precise data on landscape changes, infrastructure destruction and air pollution levels without the need of researcher’s physical presence in the military action zone, and therefore, without risk to life and health. This monitoring approach allows obtaining up-to-date information crucial for decision making on providing emergency humanitarian assistance, identifying resource requirements for potential infrastructure restoration and environmental renewal. Studying the catastrophic consequences of the russian-ukrainian war using remote sensing tools makes it possible to conduct a professional expertise of the damage and losses inflicted on Ukraine as a result of the russian aggression, and to assess the necessary resources and means for the further restoration of our country. The article reveals three significant events that took place since the beginning of the full-scale invasion and demonstrates how satellite imagery can be used to monitor the battlegrounds around the Gostomel military airfield, the territory of the Dnipro river overflow caused by the undermining of the Kakhovska HPP, and the zone heavily affected by air pollution as a result of an intentional arson at the Lysychansk oil refinery after the russian airstrike.

Remote sensing images are inevitably affected by the degradation of haze with complex appearance and non-uniform distribution, which remarkably affects the effectiveness of downstream remote sensing visual tasks. However, most current methods principally operate in the original pixel space of the image, which hinders the exploration of the frequency characteristics of remote sensing images, resulting in these models failing to fully exploit their representation ability to produce high-quality images. This paper proposes a frequency-oriented remote sensing dehazing Transformer named FOTformer, to explore information in the frequency domain to eliminate disturbances caused by haze in remote sensing images. It contains three components. Specifically, we developed a frequency-prompt attention evaluator to estimate the self-correlation of features in the frequency domain rather than the spatial domain, improving the image restoration performance. We propose a content reconstruction feed-forward network that captures information between different scales in features and integrates and processes global frequency domain information and local multi-scale spatial information in Fourier space to reconstruct the global content under the guidance of the amplitude spectrum. We designed a spatial-frequency aggregation block to exchange and fuse features from the frequency domain and spatial domain of the encoder and decoder to facilitate the propagation of features from the encoder stream to the decoder and alleviate the problem of information loss in the network. The experimental results show that the FOTformer achieved a more competitive performance against other remote sensing dehazing methods on commonly used benchmark datasets.

Oriented object detection (OOD) in remote sensing images (RSIs) is of increasing interest. Since RSIs often contain many oriented objects, it is valuable and challenging to discover geometric invariance of geospatial objects to improve the model’s perception of rotation angle and scale. In this paper, we propose a twin-tower detector (T2Det) for OOD in RSIs. Specifically, T2Det overcomes the challenges posed by the angles and scales of oriented object by developing a self-supervised (SS) branch that exploits geometric invariance based on the main branch. Then, we design a twin-tower (T2) loss function to enhance the network’s ability to perceive the geometric invariance of geospatial object, where a coarse loss function and a fine loss function are introduced for both branches to optimize the model from coarse to fine. In addition, T2 loss function optimization strategy based on global or refinement modes is developed to achieve the trade-off between the main branch and the SS branch. On three benchmark datasets, including VEDAI, HRSC2016, and NUAA-SIRST, our T2Det achieves competitive performance of 85.15%, 90.66% mAP, and 99.28 Pd, respectively, without unnecessary extra features.

In the last decade the Netherlands has experienced an economic recession. Now, in 2017, the economy is picking up again. This growth does not only come with advantages because economic growth demands more from the transport system. Congestion is increasing again, the capacity of the train system is now insufficient during peak hours, and the world faces environmental challenges that are partly due to emissions caused by travellers. These negative effects worsen as travellers make rational choices, which could be undesirable from a system, or social welfare, perspective. For example, car drivers do often not choose public transport options, because it costs them more effort; however, if they choose public transport options, then the system improves since congestion and emissions will reduce. Or another example, if travellers choose to avoid peak hours, they might not arrive at their desired time, but then they do not contribute to peak hour congestion or crowding. In addition, the capacity of the transport system is more effectively used if travellers spread out over the day. Passenger transport pricing can be an incentive for travellers to change their choices, and can therefore be used to mitigate congestion, emissions, and other undesirable effects. Passenger transport pricing is the umbrella term for measures that make passengers pay for their travels. Traditional pricing measures are for example: fuel excise taxes, public transport fares, and periodical registration fees for vehicles. More innovative measures are cordon charges (e.g., in London, Stockholm, and Singapore), special tolling lanes, and peak avoidance projects. When such an innovative measure has different prices for times of the day, and for different locations (i.e., it is time- and space-differentiated), travellers’ choices related to route, mode and departure time can be influenced. By changing these choices, the overall performance of the transport system can improve. Travellers have differences regarding time valuation, preferred departure or arrival times, and car ownership. Therefore, a measure can become even more effective if it also allows to differentiate amongst characteristics of travellers. However, innovative pricing measures have not been implemented widely across the globe, despite their potential to reduce congestion and emissions. This is primarily due to lack of public and political support. The Netherlands has experienced decades of political discourse and many failed proposals. Low public support did not contribute to (political) agreement either, because it has always fuelled the discussion with dissenting opinions. In the process of designing policies and making decisions, strategic planning models usually estimate (or forecast) the effects of the policy. The preferences of travellers and the transport system are captured by mathematical equations. Such models are always a simplified representation of reality. To apply them to asses pricing measures, they should capture the underlying mechanisms that are important for transport pricing as realistic as possible. This dissertation identifies disadvantages of current strategic network models for passenger transport pricing and provides methodological advances to resolve them. This is done with a holistic approach that combines game theory, discrete choice analysis, traffic flow theory, and tranport economics into one modelling framework. This framework has many sub-models and provides a toolbox for analysts to determine the effects of innovative pricing schemes. The basic principle for each tool is to make them realistic (so that the results are credible for decision makers), and computationally efficient. The latter means that many different pricing schemes can be computed within reasonable time. By providing the methodological advances, that are briefly discussed in the next sections, this dissertation aims to improve public and political support. For example, the preferences of multiple stakeholders can be considered, the possible conflicts between them can be identified, and solutions based on concepts that aim to resolve these conflicts can be computed.

According to the target direction, target size, shooting angle and scene diversity in remote sensing image, the target detection and recognition accuracy of remote sensing image is not high. This paper adopts a convolutional neural network(CNN) for ship targets detection and recognition in Remote Sensing Images, which is based on Faster R-CNN model. In our model, the network is optimized for the characteristics of ship targets in remote sensing images, use the multi-level proposal region extraction and multi-level feature fusion technology to construct the CNN model for ship target detection and recognition. The experimental results show that the network model has strong robustness and has a high accuracy at ship targets detection and recognition in remote sensing image.

<strong class="journal-contentHeaderColor">Abstract.</strong> Seasonal snow has crucial impacts on climate, ecosystems and humans, but it is vulnerable to global warming. The land component (ELM) of the Energy Exascale Earth System Model (E3SM), mechanistically simulates snow processes from accumulation, canopy interception, compaction, snow aging to melt. Although high-quality field measurements, remote sensing snow products and data assimilation products with high spatio-temporal resolution are available, there has been no systematic evaluation of the snow properties and phenology in ELM. This study comprehensively evaluates ELM snow simulations over the western United States at 0.125&deg; resolution during 2001&ndash;2019 using the Snow Telemetry (SNOTEL) in situ networks, MODIS remote sensing products (i.e., MCD43 surface albedo product, the spatially and temporally complete (STC) Snow-Covered Area and Grain Size (MODSCAG) and MODIS Dust and Radiative Forcing in Snow (MODDRFS) products (STC-MODSCAG/STC-MODDRFS), and the Snow Property Inversion from Remote Sensing (SPIReS) product) and two data assimilation products of snow water equivalent and snow depth (i.e., University of Arizona (UA) and SNOw Data Assimilation System (SNODAS)). Overall the ELM simulations are consistent with the benchmarking datasets and reproduce the spatio-temporal patterns, interannual variability and elevation gradients for different snow properties including snow cover fraction (<em>f</em>_sno), surface albedo (𝛼_sur) over snow cover regions, snow water equivalent (SWE) and snow depth (<em>D</em>_sno). However, there are large biases of <em>f</em>_sno with dense forest cover and 𝛼_sur in the Rocky Mountains and Sierra Nevada in winter, compared to the MODIS products. There are large discrepancies of snow albedo, snow grain size and light-absorbing particles induced snow albedo reduction between ELM and the MODIS products, attributed to uncertainties in the aerosol forcing data, snow aging processes in ELM, and remote sensing retrievals. Against UA and SNODAS, ELM has a mean bias of -20.7 mm (-35.9 %) and -20.4 mm (-35.5 %), respectively for spring, and -13.8 mm (-27.8 %) and -10.2 mm (-22.2 %), respectively for winter. ELM shows a relatively high correlation with SNOTEL SWE, with mean correlation coefficients of 0.69, but negative mean biases of -122.7 mm, respectively. Compared to the snow phenology of STC-MODSCAG and SPIReS, ELM shows delayed snow accumulation onset date by 17.3 and 12.4 days, earlier snow end date by 35.5 and 26.8 days, and shorter snow duration by 52.9 and 39.5 days. This study underscores the need for diagnosing model biases and improving ELM representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.

This paper studies the application of game theory in enhancing management practices and strategic decision-making within competitive business environments. Traditional management approaches, often rigid and hierarchical, fail to account for dynamic market interactions, necessitating a shift toward game-theoretical frameworks that model strategic interdependence among firms. By analyzing key concepts within game theory, this study demonstrates how game theory optimizes decision-making in pricing, market entry, and collaborative strategies. However, practical challenges persist, including unrealistic assumptions of perfect rationality, information asymmetry, and oversimplified models that neglect human behaviour. Thus, the paper also explores future directions, including AI-driven models and crisis management, while emphasising the need for adaptive implementation. Ultimately, integrating game theory with real-world business insights enables firms to transition from reactive to proactive strategies, fostering sustainable competitive advantages in ever-evolving industries.

Despite deep neural networks (DNNs) have been widely applied in remote sensing (RS) scene classification and achieved satisfying performance, the vulnerability of DNNs towards adversarial examples significantly degrades their performance. Moreover, the relatively limited labeled samples of RS scene classification make DNNs more likely to overfit, leading to weak generalizability and noise sensitivity. This may result in DNNs being more vulnerable to adversarial examples. Consequently, the defense of adversarial examples is of crucial importance to improve both the generalizability and robustness of DNNs in the RS scene classification task. However, few studies have been conducted on defense for RS scene classification, especially ignoring the intrinsic characteristics of RS images. In this paper, an effective defense framework for RS scene classification, named reconstruction-assisted and distance-optimized adversarial training (RDAT), is proposed to defend adversarial examples. In order to solve the problems caused by high interclass similarity, a distance-optimized (DO) strategy is designed for adversarial training to strengthen the learning of underfitting content, increase the interclass distance, and improve the robustness of the networks. Furthermore, in order to generate high quality samples for adversarial training, a reconstruction-assisted (RA) block is proposed to eliminate adversarial perturbations in adversarial examples. Specifically, in this block, by swin transformer (SwinT) block and multi-scale convolution (MSC) block, SwinT-MSC-UNet (SMUNet) is constructed to fully extract global and multi-scale local features to adapt to the characteristics of RS images with large variance of ground object scales. Extensive experiments on the benchmark datasets, i.e., UC Merced (UCM) and Aerial Image Dataset (AID), have demonstrate that the proposed RDAT can effectively resist multiple adversarial attacks and yield superior results than other defense methods for RS scene classification.

Most object detection methods based on remote sensing images are generally dependent on a large amount of high-quality labeled training data. However, due to the slow acquisition cycle of remote sensing images and the difficulty in labeling, many types of data samples are scarce. This makes few-shot object detection an urgent and necessary research problem. In this paper, we introduce a remote sensing few-shot object detection method based on text semantic fusion relation graph reasoning (TSF-RGR), which learns various types of relationships from common sense knowledge in an end-to-end manner, thereby empowering the detector to reason over all classes. Specifically, based on the region proposals provided by the basic detection network, we first build a corpus containing a large number of text language descriptions, such as object attributes and relations, which are used to encode the corresponding common sense embeddings for each region. Then, graph structures are constructed between regions to propagate and learn key spatial and semantic relationships. Finally, a joint relation reasoning module is proposed to actively enhance the reliability and robustness of few-shot object feature representation by focusing on the degree of influence of different relations. Our TSF-RGR is lightweight and easy to expand, and it can incorporate any form of common sense information. Sufficient experiments show that the text information is introduced to deliver excellent performance gains for the baseline model. Compared with other few-shot detectors, the proposed method achieves state-of-the-art performance for different shot settings and obtains highly competitive results on two benchmark datasets (NWPU VHR-10 and DIOR).

According to 230 flux site observations, intra-day and diurnal ground heat flux (G) accounted for 19.2 % and 28.8 % of the corresponding net radiation, respectively. This indicates that G plays an important role in remote sensing (RS) evapotranspiration (ET) models. The accuracy of the five G simulation methods in the surface energy balance-based RS ET models was evaluated using half-hourly observations. The linear coefficient (LC) method and the methods embedded with the normalized difference vegetation index (NDVI) were able to accurately simulate a half-hourly G series at most sites. The mean and median Nash-Sutcliffe efficiency (NSE) values of all sites were generally higher than 0.50 in each half-hour period. However, the two methods embedded by fractional vegetation coverage showed poor performance in most half-hour periods, except the sunrise and sunset periods, with the mean and median NSE values of all sites below 0.20. The accuracy of each method varied significantly at different sites and at half-hour intervals. The highest accuracy was exhibited during sunrise periods (6:00–7:00), followed by sunset periods (17:00–18:00). There were 92 % (211/230) sites with an NSE of the LC method greater than 0.50 at 6:30. It showed a slightly higher accuracy during night periods than during daytime periods. The lowest accuracy was observed at noon periods (10:00–15:30). The sites with an NSE exceeding 0.50 only accounted for 51 % (118/230) and 43 % (100/230) at 10:30 and 13:30, respectively. The accuracy of Northern Hemisphere sites was generally higher than that of Southern Hemisphere sites. In general, the highest and lowest accuracies were observed at the high- and low-latitude sites, respectively. The performance of the LC method and the methods embedded with NDVI were generally satisfactory at the Eurasian and North American sites, with the NSE values of most sites exceeding 0.70. Conversely, it exhibited relatively poor performance at the African, South American, and Oceanian sites, especially the African sites. Both the temporal and spatial distributions of the accuracy of the G simulation were positively correlated with the correlation between G and the net radiation. Although the G simulation methods accurately simulated the G series at most sites and time periods, their performance was poor at some sites and time periods. The application of RS ET datasets covering these sites requires caution. The optimal parameter value for each method varied greatly at different sites. This indicates that the fixed parameter values in the G simulation methods are not appropriate. Further improvement of G simulations at these sites and time periods is recommended for the RS ET modelers.

With the development of remote sensing (RS) and the related technologies, the resolution of RS images is enhancing. Compared with moderate or low resolution images, high-resol ution ones can provide more detailed ground information. However, a variety of terrain has complex spatial distribution. The different objectives of high-resolution images have a variety of features. The effectiveness of these features is not the same, but some of them are complementary. Considering the above information and characteristics, a new method is proposed to classify RS images based on hierarchical fusion of multi-features. Firstly, RS images are pre-classified into two categories in terms of whether feature points are uniformly or non-uniformly distributed. Then, the color histogram and Gabor texture feature are extracted from the uniformly-distributed categories, and the linear spatial pyramid matching using sparse coding (ScSPM) feature is obtained from the non-uniformly-distributed categories. Finally, the classification is performed by two support vector machine classifiers. The experimental results on a large RS image database with 2100 images show that the overall classification accuracy is boosted by 10.1% in comparison with the highest accuracy of single feature classification method. Compared with other multiple-feature fusion methods, the proposed method has achieved the highest classification accuracy on this dataset which has reached 90.1%, and the time comple xity of the algorith m is also greatly reduced.

In this paper, motivated by the need of a quantitative model for the power and rate control in IEEE 802.11 WLANs and the lack of related work in the literature, we address the problem by establishing a quantitative game theoretic framework. Our motivation of using game theoretic approach rather than global optimization approach is two-fold: 1) game theory is a powerful tool to model selfish behaviors and their impact on the system performance in distributed environments with self-interested players; 2) game theory can model the features or constraints of IEEE 802.11 WLANs such as lack of coordination and network feedback.

Hyperspectral image (HSI) classification is one of the growing research areas in Remote Sensing. The high dimensionality of the data cube and a high correlation among the hyperspectral bands, poses a difficulty in HSI processing, and selection is one of the promising solutions to address the issue. In this research paper, a whale optimization-based band selection is proposed. Initially, the informative bands are retrieved via a search mechanism which resembles the hunting behaviour of the humpback whales. Later, a hybrid filter is proposed to extract the intrinsic and edge preserving spatial features. Finally, the smoothened bands are trained using a non-linear support vector machine for efficient classification. The exploitation and exploration in the local and global search for the selection of whale hyperspectral bands from the search space proved to be useful in achieving the high-quality classification maps. The overall accuracy reported on the three benchmark data sets – Indian Pines, University of Pavia and Salinas are 99.05, 97.88, and 97.56%, respectively. The proposed method accuracy is achieved with 5% fewer bands on Indian Pines, 70% on University of Pavia and 50% on Salinas Datasets over the other competing methods, which show the effectiveness of the whale optimization-based band selection.