Multimodal data fusion can provide valuable and diverse information for remote sensing image segmentation. However, different modal data have different feature distributions, which causes some conflicts and redundancies in cross-modal feature fusion. In addition, existing multimodal fusion networks usually adopt a two-branch structure with a large number of parameters and high computational cost. To address these problems, we propose a multiscale cross-modal knowledge transfer network (MCKTNet) for remote sensing image segmentation. First, we use a cross-modal migration learning method that combines channel discrete loss and spatial discrete loss to facilitate cross-modal migration of geometric and semantic features and reduces redundancy by minimizing the differences in feature distribution. Then, we use the polarized cross-self-attention mechanism to establish long-range correlations between different modal features across spatial and channel dimensions, and only a small number of parameters need to be added to achieve complementary cross-modal feature fusion. Finally, to accurately capture object edges, we propose a multiscale edge perception module to optimize the edge details in the prediction results at the pixel level. Extensive experiments demonstrate that the proposed method is effective, robust, and generalizable, and achieves state-of-the-art performance in multiple remote sensing image semantic segmentation tasks with only 13.51 million parameters. The code will be available athttps://github.com/NUAALISILab/MCKTNet.

Recently, deep learning has shown great potential in areas such as infrared small target detection, but due to the lack of sample datasets, especially the public infrared small target dataset, model training, and extensive research have been limited. Synthetic data that contains a lot of information about the shape and scene of the target is widely used to augment real-world data. However, due to the domain shift between the real and synthetic data domains, combining them directly may not lead to significant improvements or even worse results. In this paper, we propose a semantic domain adaptive framework for cross-domain infrared small target detection (SDAISTD), which effectively decreases the domain shift between the real and synthetic data domains, leading to better training and detection results. Specially, SDAISTD uses a supervised learning adaptation approach from the feature perspective. The domain shift of cross-domain is diminished by extracting domain invariant features to align different feature distributions in the feature space. Additionally, we propose a semantic feature alignment loss function that effectively mitigates semantic information misalignment and aligns category features. Extensive experiments and analyses conducted on two baselines demonstrate the generality and validity of our proposed framework. Remarkably, our framework outperforms several representative baseline models in the new State-Of-The-Art records.

As spatial resolution increases, the information conveyed by remote sensing images becomes more and more complex. Large-scale variation and highly discrete distribution of objects greatly increase the challenge of the semantic segmentation task for remote sensing images. Mainstream approaches usually use implicit attention mechanisms or Transformer modules to achieve global context for good results. However, these approaches fail to explicitly extract intra-object consistency and inter-object saliency features leading to unclear boundaries and incomplete structures. In this paper, we propose a Category-Guided Global-Local Feature Interaction Network (CGGLNet), which utilizes category information to guide the modeling of global contextual information. To better acquire global information, we proposed a Category-Guided Supervised Transformer module (CGSTM). This module guides the modeling of global contextual information by estimating the potential class information of pixels so that features of the same class are more aggregated and those of different classes are more easily distinguished. To enhance the representation of local detailed features of multi-scale objects, we designed the Adaptive Local Feature Extraction Module (ALFEM). By parallel connection of the CGSTM and the ALFEM, our network can extract rich global and local context information contained in the image. Meanwhile, the designed Feature Refinement Segmentation Head (FRSH) helps to reduce the semantic difference between deep and shallow features and realizes the full integration of different levels of information. Extensive ablation and comparison experiments on two public remote sensing datasets (ISPRS Vaihingen dataset and ISPRS Potsdam dataset) indicate that our proposed CGGLNet achieves superior performance compared to the state-of-the-art methods.

Due to differences in acquisition, domain shift is prevalent between different hyperspectral scene, and conventional classification models usually have poor generalization performance in cross-scene case. Domain generalization allows models trained in the source domain to be applied directly to the unseen domain, thus it is an important way to address cross-scene classification problems. Most of the existing methods eliminate domain shift by aligning the output space. However, due to the possible loss of information in the output space, enforcing consistency of distributions in the output space alone is not enough to ensure that distributions between different domains are correctly aligned. To alleviate the above problems, we propose a two-stage domain alignment single-source domain generalization network for cross-scene hyperspectral images classification based on generative domain adversarial networks. First, to reduce the negative impact of noise in data cube on pseudo domain generation, a spectral learning branch is introduced to guide generator training. Meanwhile, to enhance the ability of the discriminator to extract joint spatial-spectral features, a pyramid feature fusion network with double projection heads is proposed. Finally, combining the double projection head of the discriminator, we propose a two-stage domain alignment loss function to better eliminate domain shift. Several experiments on three public datasets demonstrate that the proposed method has competitive advantage in classification. The code will be available at:https://github.com/XiaozhenWang-NUAA/TSDAnet.

Geospatial object segmentation is a fundamental task in remote sensing image interpretation. Although deep learning has shown great potential for this task, it often suffers from limited receptive fields, insufficient global feature extraction ability, and inaccurate edge positioning, resulting in low accuracy and errors in the results. In this letter, we propose a novel Global Context Dependency Awareness Network (GCDNet) to achieve high-accuracy segmentation results. To overcome the limited receptive fields and promote feature extraction ability, we propose a new dot product attention mechanism to establish long-distance dependencies between different receptive field feature maps. To achieve more accurate object edges, we design an edge-aware optimization module to guide the operation to directly optimize the edge details from the prediction result at the pixel level. Extensive experiments on two well-known public high-resolution remote sensing image datasets have been conducted to verify the performance of the proposed method, and the results show that the proposed method has significant advantages and maintains its robustness in different cases. Code will be available at: https://github.com/Cuiadd/GCDNet.

Mobile data offloading enables the mobile network operator (MNO) to deal with the explosive growth of cellular data by leasing third‐party access points (APs) to partially deliver the mobile traffic. This study proposes a novel incentive framework for the mobile data offloading market under QoE‐aware users. Considering user satisfaction, the authors formulate the interaction among the MNO, APs, and offloaded users as a three‐stage Stackelberg game. Through the Stackelberg game, the APs determine their optimal contributions via the best response method and the offloaded users determine their optimal accepted prices via the proposed dynamic pricing mechanism. Then the MNO makes its decision for profit maximisation. Furthermore, based on contract theory, an optimal dynamic scheme between the MNO and the remaining users is established. Under the dynamic scheme, they prove the personal rationality and incentive compatibility properties. Moreover, the optimisation contract problem is transformed into a relaxed contract problem, and the proposed dynamic algorithm is subsequently used to handle non‐feasible solutions. Thus, the proposed framework can improve user satisfaction without affecting MNO profits. Simulation results show that the proposed framework can achieve better performances in terms of user satisfaction and MNO profits compared with traditional algorithms.