The coordination of distributed energy resources (DERs) within virtual power plants (VPPs) is expected to generate significant economic benefits and enhance the operational stability of modern power systems. However, achieving massive coordination of heterogeneous and uncertain DERs remains a challenge in current research. To address this issue, this article proposes a novel bi-level optimization approach based on mean-field reinforcement learning (MFRL) to enable the coordination of massive DERs in VPPs. The problem is decomposed into multiple subproblems: the upper-level subproblem models power dispatch among integrated energy systems (IESs) in response to coordinated demand, while a series of lower-level subproblems determine the operational schemes of DERs within individual IESs. Considering the large decision space, an MFRL algorithm with fast Shapley credit allocation is developed to efficiently solve the upper-level optimization. Meanwhile, the lower-level subproblems are formulated as small-scale mixed-integer linear programming (MILP) problems, addressing the difficulties caused by IES heterogeneity in applying mean-field approximation. Simulation results show that the proposed approach significantly improves convergence speed and reduces the global cost of VPP operation, especially in massive-scale scenarios. In test scenarios ranging from 10 to 500 agents, the proposed bi-level optimization approach improves the objective by 4.8%-26.6%, compared to the advanced baseline method.

Recently, deep learning methods have gained remarkable achievements in the field of image restoration for remote sensing (RS). However, most existing RS image restoration methods focus mainly on conventional first-order degradation models, which may not effectively capture the imaging mechanisms of remote sensing images. Furthermore, many RS image restoration approaches that use deep learning are often criticized for their lacks of architecture transparency and model interpretability. To address these problems, we propose a novel progressive restoration network for high-order degradation imaging (HDI-PRNet), to progressively restore different image degradation. HDI-PRNet is developed based on the theoretical framework of degradation imaging, also Markov properties of the high-order degradation process and Maximum a posteriori (MAP) estimation, offering the benefit of mathematical interpretability within the unfolding network. The framework is composed of three main components: a module for image denoising that relies on proximal mapping prior learning, a module for image deblurring that integrates Neumann series expansion with dual-domain degradation learning, and a module for super-resolution. Extensive experiments demonstrate that our method achieves superior performance on both synthetic and real remote sensing images.

When the buildings themselves or the environments they are located in changes, it poses a cross-domain problem for building extraction. Since the semantics of same category should be consistent across domains, a memory mechanism can be used to drive models to capture it. A key prerequisite for a satisfactory memory mechanism is that the memory content is highly task-relevant, i.e., the memory mechanism represents domain-invariant semantic features of buildings as much as possible. It is worth noting that images from different domains have diverse styles, which can interfere with the representation of domain-invariant semantic features of buildings. To maximize the effectiveness of memory mechanisms in cross-domain building extraction tasks, it is necessary to reduce the interference of style information. Therefore, we propose a cross-domain extraction method for buildings based on decoupling style-semantic feature. It expresses the image style features and building semantic features separately and optimizes the domain-invariant features of buildings by constraining their relationship through an orthogonal loss. Specifically, on the one hand, style features within the same domain as well as semantic features within and across domains are narrowed down through contrastive learning to ensure the consistency of style features extracted within the same domain and the consistency of semantics across domains. On the other hand, the style features and domain-invariant semantic features in memory mechanism can be stored and reused to fully exploit self-supervised information. Results of the cross-domain experiments show that the proposed method can achieve optimal building extraction.

With the increasing number of spaceborne synthetic aperture radar (SAR) systems, traditional absolute radiometric calibration methods based on calibration fields are becoming increasingly difficult to meet the demand for long-term monitoring of radiometric accuracy in spaceborne SAR images. SAR radiometric cross-calibration methods based on the stability characteristics of big data are expected to solve this problem. In this study, a space-borne SAR radiometric cross-calibration method considering typical scattering effects in building areas was proposed by analyzing the imaging mechanism of SAR images of building areas. The experiment using Sentinel-1A/-1B data has verified that extracting stable pixels based on this method can effectively improve the stability of the calibration benchmark, and it has been determined that the mean centroid of building areas based on the sliding window is a more stable calibration benchmark. The stability of this calibration benchmark is 0.23 dB, which is more stable than that of tropical rainforests; In addition, a SAR radiometric cross-calibration scheme was designed, and experiments were conducted based on this scheme. The experimental results showed that the absolute calibration accuracy of cross-calibration on the same satellite was 0.25 dB, and that of cross-calibration on different satellites was 0.36 dB.

Remote sensing images are essential for many applications of the Earth’s sciences, but their quality can usually be degraded due to limitations in sensor technology and complex imaging environments. To address this, various remote sensing image deblurring methods have been developed to restore sharp and high-quality images from degraded observational data. However, most traditional model-based deblurring methods usually require predefined hand-crafted prior assumptions, which are difficult to handle in complex applications. On the other hand, deep learning-based deblurring methods are often considered as black boxes, lacking transparency and interpretability. In this work, we propose a new blind deblurring learning framework that utilizes alternating iterations of shrinkage thresholds. This framework involves updating blurring kernels and images, with a theoretical foundation in network design. Additionally, we propose a learnable blur kernel proximal mapping module (KPMM) to improve the accuracy of the blur kernel reconstruction. Furthermore, we propose a deep proximal mapping module in the image domain, which combines a generalized shrinkage threshold with a multiscale prior feature extraction block. This module also incorporates an attention mechanism to learn adaptively the importance of prior information, improving the flexibility and robustness of prior terms, and avoiding limitations similar to hand-crafted image prior terms. Consequently, we design a novel multiscale generalized shrinkage threshold network (MGSTNet) that focuses specifically on learning deep geometric prior features to enhance image restoration. Experimental results on real and synthetic remote sensing image datasets demonstrate the superiority of our MGSTNet framework compared to existing deblurring methods.

Abstract Efficient and accurate semantic segmentation is crucial for autonomous driving scene parsing. Capturing detailed information and semantic information efficiently through two‐branch networks has been widely utilised in real‐time semantic segmentation. This study proposes a network named MRFNet based on two‐branch strategy to solve the problem of accuracy and speed of segmentation in urban scenes. Many real‐time networks do not comprehensively consider contextual information from sub‐regions in different directions and at different scales. To handle this problem, a Multi‐directional Feature Refinement Module (MFRM) which has three sub‐paths to capture information at different scales and directions is proposed. And MFRM reduces computation by using strip pooling and dilated convolution operations. In particular, the authors propose a Feature Cross‐guide Aggregation Module to aggregate detailed information and contextual information through the mutual guidance of detailed information and semantic information. This module guides the extraction of feature maps in a more precise direction. Experiments on Cityscapes and CamVid datasets demonstrate the effectiveness of our method by achieving a balance between accuracy and inference speed. Specially, on single 1080Ti GPU, our method yields 78.9% mean intersection over union (mIoU) and 77.4% mIoU at speed of 144.5 frames per second (FPS) and 120.8 FPS on Cityscapes and CamVid datasets respectively.

The traditional auto-calibration of synthetic aperture radar (SAR) images based on the range-Doppler (RD) model couples the coordinates of ground target points (GTPs) and the slant range correction to solve, resulting in unstable solutions. This paper proposes a twofold positioning method (TPM) for multiview spaceborne SAR images to solve this problem. In order to obtain the more precise coordinates of GTPs and the stability of the solution, the conjugate gradient method (CGM) is performed to the initial and secondary positioning for the normalized RD model. Compared with the traditional least squares method (LSM), the accuracy of TPM is on average 13.47% higher with YaoGan-SAR satellite (150MHz and 24.4us), and 44.38% higher with YaoGan-SAR satellite (200MHz and 24.4us). In addition, the stability of the method is improved. The experimental results based on the YaoGan-SAR satellite images verify the effectiveness of the method.

Multi-modal medical image fusion is a challenging yet important task for precision diagnosis and surgical planning in clinical practice. Although single feature fusion strategy such as Densefuse has achieved inspiring performance, it tends to be not fully preserved for the source image features. In this paper, a deep multi-fusion framework with classifier-based feature synthesis is proposed to automatically fuse multi-modal medical images. It consists of a pre-trained autoencoder based on dense connections, a feature classifier and a multi-cascade fusion decoder with separately fusing high-frequency and low-frequency. The encoder and decoder are transferred from MS-COCO datasets and pre-trained simultaneously on multi-modal medical image public datasets to extract features. The feature classification is conducted through Gaussian high-pass filtering and the peak signal to noise ratio thresholding, then feature maps in each layer of the pre-trained Dense-Block and decoder are divided into high-frequency and low-frequency sequences. Specifically, in proposed feature fusion block, parameter-adaptive pulse coupled neural network andl1-weighted are employed to fuse high-frequency and low-frequency, respectively. Finally, we design a novel multi-cascade fusion decoder on total decoding feature stage to selectively fuse useful information from different modalities. We also validate our approach for the brain disease classification using the fused images, and a statistical significance test is performed to illustrate that the improvement in classification performance is due to the fusion. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance in both qualitative and quantitative evaluations.