Automatic extraction of road information from remote sensing images is widely used in many fields, such as urban planning and automatic navigation. However, due to interference from noise and occlusion, the existing road extraction methods can easily lead to road discontinuity. To solve this problem, a road extraction network with bidirectional spatial information reasoning (BSIRNet) is proposed, in which neighbourhood feature fusion is used to capture spatial context dependencies and expand the receptive field, and an information processing unit with a recurrent neural network structure is used to capture channel dependencies. BSIRNet enhances the connectivity of road information through spatial information reasoning. Using the public Massachusetts road dataset and Wuhan University road dataset, the superiority of the proposed method is verified by comparing its results with those of other models.

Radiation anomalies in optical remote sensing images frequently occur due to electronic issues within the image sensor or data transmission errors. These radiation anomalies can be categorized into several types, including CCD, StripeNoise, RandomCode1, RandomCode2, ImageMissing, and Tap. To ensure the retention of image data with minimal radiation issues as much as possible, this paper adopts a self-made radiation dataset and proposes a FlexVisionNet-YOLO network to detect radiation anomalies more accurately. Firstly, RepViT is used as the backbone network with a vision transformer architecture to better capture global and local features. Its multiscale feature fusion mechanism efficiently handles targets of different sizes and shapes, enhancing the detection ability for radiation anomalies. Secondly, a feature depth fusion network is proposed in the Feature Fusion part, which significantly improves the flexibility and accuracy of feature fusion and thus enhances the detection and classification performance of complex remote sensing images. Finally, Inner-CIoU is used in the Head part for edge regression, which significantly improves the localization accuracy by finely adjusting the target edges; Slide-Loss is used for classification loss, which enhances the classification robustness by dynamically adjusting the category probabilities and markedly improves the classification accuracy, especially in the sample imbalance dataset. Experimental results show that, compared to YOLOv8, the proposed FlexVisionNet-YOLO method improves precision, recall, mAP0.5, and mAP0.5:0.9 by 3.5%, 7.1%, 4.4%, and 13.6%, respectively. Its effectiveness in detecting radiation anomalies surpasses that of other models.

The extraction of road information from high-resolution remotely-sensed images has important application value in many fields. Rural roads have the characteristics of relatively narrow widths and diversified pavement materials; these characteristics can easily lead to problems involving the similarity of the road texture with the texture of surrounding objects and make it difficult to improve the automation of traditional high-precision road extraction methods. Based on this background, a semi-automatic rural road extraction method constrained by a combination of geometric and texture features is proposed in this paper. First, an adaptive road width extraction model is proposed to improve the accuracy of the initial road centre point. Then, aiming at the continuous change of curvature of rural roads, a tracking direction prediction model is proposed. Finally, a matching model under geometric texture constraints is proposed, which solves the problem of similarity between road and neighbourhood texture to a certain extent. The experimental results show that by selecting different types of experimental scenes or remotely sensed image data, compared with other methods, the proposed method can not only guarantee the road extraction accuracy but also improve the degree of automation to a certain extent.

Existing Generative Adversarial Networks (GANs) frequently yield remote sensing images with blurred fine details, distorted textures, and compromised spatial structures when applied to super-resolution (SR) tasks, so this study proposes a Multi-Attention Fusion Generative Adversarial Network (MAF-GAN) to address these limitations: the generator of MAF-GAN is built on a U-Net backbone, which incorporates Oriented Convolutions (OrientedConv) to enhance the extraction of directional features and textures, while a novel co-calibration mechanism—incorporating channel, spatial, gating, and spectral attention—is embedded in the encoding path and skip connections, supplemented by an adaptive weighting strategy to enable effective multi-scale feature fusion, and a composite loss function is further designed to integrate adversarial loss, perceptual loss, hybrid pixel loss, total variation loss, and feature consistency loss for optimizing model performance; extensive experiments on the GF7-SR4×-MSD dataset demonstrate that MAF-GAN achieves state-of-the-art performance, delivering a Peak Signal-to-Noise Ratio (PSNR) of 27.14 dB, Structural Similarity Index (SSIM) of 0.7206, Learned Perceptual Image Patch Similarity (LPIPS) of 0.1017, and Spectral Angle Mapper (SAM) of 1.0871, which significantly outperforms mainstream models including SRGAN, ESRGAN, SwinIR, HAT, and ESatSR as well as exceeds traditional interpolation methods (e.g., Bicubic) by a substantial margin, and notably, MAF-GAN maintains an excellent balance between reconstruction quality and inference efficiency to further reinforce its advantages over competing methods; additionally, ablation studies validate the individual contribution of each proposed component to the model’s overall performance, and this method generates super-resolution remote sensing images with more natural visual perception, clearer spatial structures, and superior spectral fidelity, thus offering a reliable technical solution for high-precision remote sensing applications.

Infrared–visible image matching is a prerequisite for environmental monitoring, military reconnaissance, and multisource geospatial analysis. However, pronounced texture disparities, intensity drift, and complex non-linear radiometric distortions in such cross-modal pairs mean that existing frameworks such as SuperPoint + SuperGlue (SP + SG) and LoFTR cannot reliably establish correspondences. To address this issue, we propose a dual-path architecture, the Environment-Adaptive Wavelet Enhancement and Radiation Priors Aided Matcher (EWAM). EWAM incorporates two synergistic branches: (1) an Environment-Adaptive Radiation Feature Extractor, which first classifies the scene according to radiation-intensity variations and then incorporates a physical radiation model into a learnable gating mechanism for selective feature propagation; (2) a Wavelet-Transform High-Frequency Enhancement Module, which recovers blurred edge structures by boosting wavelet coefficients under directional perceptual losses. The two branches collectively increase the number of tie points (reliable correspondences) and refine their spatial localization. A coarse-to-fine matcher subsequently refines the cross-modal correspondences. We benchmarked EWAM against SIFT, AKAZE, D2-Net, SP + SG, and LoFTR on a newly compiled dataset that fuses GF-7, Landsat-8, and Five-Billion-Pixels imagery. Across desert, mountain, gobi, urban and farmland scenes, EWAM reduced the average RMSE to 1.85 pixels and outperformed the best competing method by 2.7%, 2.6%, 2.0%, 2.3% and 1.8% in accuracy, respectively. These findings demonstrate that EWAM yields a robust and scalable framework for large-scale multi-sensor remote-sensing data fusion.

In response to issues with high-resolution imagery from the ZY-3 satellite—such as blurred cloud boundaries and incomplete segmentation of thin clouds following training on the OmniCloudMask (OCM) baseline model, as well as the tendency for clouds detected against high-brightness backgrounds such as snow-covered terrain to blend with the background—this paper proposes a hybrid CNN–Transformer network, HyTC-Net, designed to enhance cloud texture recognition capabilities. Based on the ConvNext-Tiny backbone, this model innovatively incorporates the CloudTextureAttention (CTA) module and a lightweight Transformer module, LightweightMobileViT (LM), while combining ASPP with a deep supervision (DS) mechanism to enhance the perception of cloud textures and boundaries, thereby improving segmentation capabilities for complex scenarios such as thick clouds, thin clouds, clouds in high-brightness scenes, and patchy clouds. On the ZY-3 dataset, HyTC-Net achieved accuracy of 94.73 ± 0.18% and a precision of 92.28 ± 0.21%, significantly outperforming the baseline model OCM by 1.12 percentage points in accuracy and 2.4 percentage points in precision. Compared with mainstream models, such as UNet and DeepLabV3+, the accuracy improved by up to 6.6 percentage points and the precision by up to 17.9 percentage points. The results demonstrate that HyTC-Net can effectively address challenges arising from uneven textures in high-resolution imagery and limited spectral information, such as unclear boundaries, misclassification of highly reflective features, and failure to detect thin clouds.