Clouds in optical remote sensing images (ORSI) significantly limit image utilization. Traditional cloud removal methods using single or multi-temporal data sources struggle to ensure reliable reconstruction for thick cloud areas. Synthetic Aperture Radar (SAR) images are increasingly used to recover information obscured by clouds, but their performance in cloud-obscured regions is unstable. Therefore, an adaptive cloud removal network for remote sensing images, named DG2-TCR, is proposed based on SAR-driven dual-flow fusion guidance (DFG). DG2-TCR uses SAR and ORSI to construct DFG, including local spatial-spectral feature reconstruction (LSSFR) flow and global texture feature compensation (GTFC). LSSFR, driven by ORSI and SAR, efficiently extracts useful features in non-cloud areas and focuses on local information reconstruction using the designed spatial-spectral features inference reconstruction block (SSIRB). Based on SAR images, GTFC guides the compensation of global texture information. DFG can adaptively extract features and reconstruct missing information from local and global scales. The public SEN12MS-CR-TS dataset is divided into four sub-datasets with different coverage to evaluate the recovering capability in varying clouds. Experiments show that the PSNR, SSIM, RMSE, FID, and NCC indicator values on four sub-datasets and the SIMLE-CR dataset are better than the seven comparison methods. Furthermore, the ablation experiments show that the generalization and robustness of this proposed method on images with different cloud coverage are better than other comparison methods. Therefore, DG2-TCR can reliably recover information on cloud occlusions with various coverage and thickness, which is significant for cloud removal in practical applications.

The global cloud detection of high-resolution remote sensing images (HRSI) is crucial for acquiring high-quality imagery and optimizing data utilization. Traditional cloud detection models, which rely on limited samples and fully supervised learning, struggle to adapt to cross-temporal and cross-spatial domains. While current unsupervised domain adaptation (UDA) methods improve performance in cross-domain cloud detection to some extent, generating high-quality, reliable pseudo-labels remains a significant challenge for global cloud detection. Therefore, this paper proposes a progressive pseudo-label learning for cross-domain cloud detection in high-resolution remote sensing (PTDA). Firstly, we propose an online domain-invariant feature guided pseudo-label generation (OPLG) strategy and learning intra-domain unaligned features (LIUF), which effectively integrate domain-invariant features and intra-domain semantics to generate high-quality pseudo-labels at the feature level. LIUF then refines the pseudo-label quality at the pixel level. Secondly, during the model training, pseudo-label constrained intra-domain feature mining loss(PCIF Loss) is designed to suppress noisy semantic information within the domain, the hole effect of thick/thin clouds, and the noise interference of the contour boundary. Four cloud detection datasets, including MS Cloud (MS), HRC WHU Cloud (WHU), 95 Cloud(95), and WHUS2-CD+(S2), are grouped into three cross-domain tests, MS2WHU, MS2S2, and WHU295. Our approach achieved the best performance with mIoU 63.99%, 58.14%, 58.82%, and OA 80.03%, 79.36%, 80.49%, respectively. The experimental results show that the proposed method outperforms seven state-of-art cross-domain comparison methods. Thus, our method has important application value for cross-domain cloud detection. The available code can be downloaded from https://github.com/gasking/PTDA.

Federated graph learning (FGL) has been proposed to collaboratively train the increasing graph data with graph neural networks (GNNs) in a recommendation system. Nevertheless, implementing an efficient recommendation system with FGL still faces two primary challenges, i.e., limited communication bandwidth and non-IID local graph data. Existing works typically reduce communication frequency or transmission amount, which may suffer significant performance degradation under non-IID settings. Furthermore, some researchers propose to share the underlying structure among clients, which brings massive communication cost. To this end, we propose an efficient FGL framework, named FedACS, which adaptively selects a subset of clients for model training, to alleviate communication overhead and non-IID issues simultaneously. In FedACS, the global GNN model learns significant hidden edges and the structure of graph data among selected clients, enhancing recommendation efficiency. This capability distinguishes it from the traditional FL client selection methods. To optimize the client selection process, we introduce a multi-armed bandit (MAB) based algorithm to select participating clients according to the resource budgets and the training performance (i.e., RMSE). Experimental results indicate that FedACS improves RMSE by 5.4% over baselines with the same resource budget and reduces communication costs by up to 70.7% to achieve the same RMSE performance.

Federated graph learning (FGL) has been proposed to collaboratively train the increasing graph data with graph neural networks (GNNs) in a recommendation system, aggregating the features of graph nodes and edges among these nodes. Nevertheless, implementing an efficient recommendation system with FGL still faces two primary challenges, i.e., limited communication bandwidth and non-IID local graph data. Existing works typically reduce communication frequency or transmission amount, which may suffer significant performance degradation under non-IID settings. Furthermore, some researchers propose to share the underlying structure information among all clients, which brings massive communication cost. To this end, we propose an efficient FGL framework, named FedACS, which adaptively selects a subset of clients for model training, to alleviate communication overhead and non-IID issues simultaneously. In FedACS, the global GNN model can learn significant hidden edges and the structure of graph data among selected clients, enhancing recommendation efficiency. This capability distinguishes it from the traditional FL client selection methods. To optimize the client selection process, we introduce a multi-armed bandit (MAB) based algorithm to select participating clients according to the resource budgets and the training performance (i.e., RMSE) under different data distributions. Experimental results show that, given the same resource budget, FedACS achieves the RMSE improvement of 5.4% over the baselines. Besides, when achieving the same RMSE performance, FedACS saves up to approximately 70.7% communication cost, compared with the baselines.

Cloud detection in high-resolution remote sensing images (HRSI) is widely applied to cross-spatiotemporal domains with various scenarios change. However, cloud detection semantic segmentation models based on limited samples cannot ensure the consistency of data distribution between the source domain (SD) and the target domain (TD), resulting in a decrease in cross-domain segmentation accuracy and robust ability. Therefore, this paper proposed a two-stage domain adaptation based on the image and feature levels (TDAIF) cloud detection framework. TDAIF designs a pseudo-target domain data generator (PTDDG) at the image level to fuse the SD foreground and TD background information effectively, assisting the model in mining invariant semantic knowledge of the TD. Then, a domain discriminator and self-ensembling joint (DDSEJ) framework is explored at the feature level to implicitly handle the alignment of global features and the optimization of decision boundaries-local features. TDAIF ultimately weakens the impact of image radiation diversity and scale divergence and improves the adaptive processing capabilities for cross-spatiotemporal data. Horizontal and internal comparative experiments on TDAIF were conducted on three domain transfer data. Experimental results show that TDAIF dramatically reduces the network accuracy loss in cross-domain. Compared with CycleGAN and AdaptSegNet, the IoU is improved by about 30%. TDAIF performs better than state-of-the-art computational visual domain adaptation methods, indicating that hierarchical data alignment from the image to the feature level is very effective.

Due to the external environment, the support positioning device (SPD) of the railway catenary is prone to break down. Therefore, the automatic detection of SPDs is of great research significance to ensure the safe operation of the railway system. However, the small-scale SPD expresses weak features due to the complexity of railway scenes. Therefore, we propose a new algorithm for automatically extracting multiple-type SPDs quickly. Firstly, a preprocessing approach of equidistant scene segmentation is proposed based on trajectory points to divide the whole scene into several independent region units for batch processing. Then, a new point spacing-based spatial clustering of applications with noise (PSBSCAN) algorithm is proposed for the extraction of pillars in the pillar corridor of each region unit. Adopting point spacing as the core standard and the parallel iteration mode with only accessing edge points can reduce the influence of the uneven point cloud density and the large-scale point clouds. Next, to locate SPDs, the spatial relationship between the trajectory point, the pillar, and the SPD in the scene is introduced into the corresponding objects to build the spatial index with semantic information. Finally, contact wires installed on the SPD are filtered through multiple-level voxel segmentation based on octree to extract SPD accurately. Through the test of 10km railway datasets, the averageF1by the proposed method is 98.61%, and the average recognition rate of SPD is 99.65%, which shows that this method can achieve efficient extraction of multiple-type SPDs in railway scenes with different point densities.