Thermal Power Plants (TPPs), as widely used industrial facilities for electricity generation, represent a key task in remote sensing image interpretation. However, detecting TPPs remains a challenging task due to their complex and irregular composition. Many traditional approaches focus on detecting compact, small-scale objects, while existing composite object detection methods are mostly part-based, limiting their ability to capture the structural and textural characteristics of composite targets like TPPs. Moreover, most of them rely on single-modality data, failing to fully exploit the rich information available in remote sensing imagery. To address these limitations, we propose a novel Cross-Modal Frequency-Responsive Attention Network (CFRANet). Specifically, the Modality-Aware Fusion Block (MAFB) facilitates the integration of multi-modal features, enhancing inter-modal interactions. Additionally, the Frequency-Responsive Attention (FRA) module leverages both spatial and localized dual-channel information and utilizes Fourier-based frequency decomposition to separately capture high- and low-frequency components, thereby improving the recognition of TPPs by learning both detailed textures and structural layouts. Experiments conducted on our newly proposed AIR-MTPP dataset demonstrate that CFRANet achieves state-of-the-art performance, with a ${\mathrm{mAP}}_{50}$ of 82.41%.

Accurate segmentation of coastal aquaculture ponds from high-resolution remote sensing images is critical for applications such as coastal environmental monitoring, land use mapping, and infrastructure management. Semi-supervised learning (SSL) has emerged as a promising paradigm by leveraging labeled and unlabeled data to reduce annotation costs. However, existing SSL methods often suffer from pseudo-label quality degradation, manifested as boundary adhesion and intra-class inconsistencies, which significantly affect segmentation accuracy. To address these challenges, we propose Semi-BSU, a boundary-aware semi-supervised semantic segmentation framework based on the mean teacher architecture. Semi-BSU integrates two novel components: (1) a Boundary Consistency Constraint (BCC), which employs an auxiliary boundary classifier to enhance contour accuracy in pseudo labels, and (2) a Superpixel Refinement Module (SRM), which refines pseudo labels at the superpixel level to improve intra-class consistency. Comprehensive experiments conducted on GF6 and ZY1E high-resolution remote sensing imagery, covering diverse coastal environments with complex geomorphological features, demonstrate the effectiveness of our approach. With half of the training set labeled, Semi-BSU achieves an MIOU of 0.8606, F1 score of 0.8896, and Kappa coefficient of 0.8080, outperforming state-of-the-art methods including CPS, GCT, and UniMatch by 0.3–4.9% in MIOU. The method maintains a compact computational footprint with only 1.81 M parameters and 55.71 GFLOPs. Even with only 1/8 labeled data, it yields a 3.57% MIOU improvement over the supervised baseline. The results demonstrate that combining boundary-aware learning with superpixel-based refinement offers an effective and efficient strategy for high-quality pseudo-label generation and accurate mapping of coastal aquaculture ponds in remote sensing imagery.

The Glimmer Imager of Urbanization (GIU) on SDGSAT-1 provides high-resolution and global-coverage images of night-time lights (NLs) with 10 m panchromatic (PAN) and 40 m multispectral (MS) imagery. High-resolution 10 m MS NL images after ideal fusion can be used to better study subtle manifestations of human activities. Most existing remote sensing image-fusion methods are based on the fusion of daytime optical remote sensing images, which do not apply to lossless compressed images of the GIU. To address this limitation, we propose a novel approach for 10 m NL data fusion, namely, a GIU NL image fusion model based on PAN-optimized OIS (OIS) and DDF (DDF) fusion for SDGSAT-1 high-resolution products. The OIS of PAN refers to the optimized stretching method that integrates linear and gamma stretching while DDF indicates a fusion process that separately merges the dark and light regions of NL images using different fusion methods, then stitches them together. In this study, fusion experiments were conducted in four study areas—Beijing, Shanghai, Moscow, and New York—and the proposed method was compared to traditional methods using visual evaluation and five quantitative evaluation metrics. The results demonstrate that the proposed method achieves superior visual quality and outperforms conventional methods across all quantitative metrics. Additionally, the ablation study confirmed the necessity of the methodological steps employed in this study.