Remote sensing image super-resolution (RSISR) techniques play a crucial role in various remote sensing applications. However, deep learning-based methods applied to RSISR encounter difficulties in learning complex features of remote sensing images and modeling long-term correlations between pixels. This study proposes a multiscale sliding window attention generation adversarial network (MSWAGAN), which combines the advantages of convolutional neural networks (CNNs) and transformers to overcome these limitations. The MSWAGAN consists of three main parts. In the shallow feature extraction part, CNN is used to extract shallow features from remote sensing images. The deep feature extraction part is divided into two stages. First, a multiscale sliding window attention (MSWA) is designed to replace the multihead attention (MHA) in the transformer. MSWA can learn local multiscale complex features of remote sensing images without increasing the number of parameters in MHA. Then, the transformer is utilized to learn global image features and model the long-range correlations between pixels. The image reconstruction part utilizes subpixel convolution for feature upsampling. Furthermore, in order to extend the application of super-resolution (SR) remote sensing images, a cross-sensor real multispectral RSISR dataset consisting of Landsat-8 (L8) and Sentinel-2 (S2) images was constructed, and a series of experiments to improve the spatial resolution of L8 images from 30 to 10 m in B, G, R, and near-infrared (NIR) bands were conducted. Experimental results demonstrate that our method outperforms some of the latest SR methods.

In the absence of high-resolution sensors, super-resolution( SR) algorithms for remote sensing imagery improve the spatial resolution of the images. Currently, most of the SR algorithms are based on deep learning methods e.g., convolutional neural networks(CNN). Particularly, the generative adversarial networks(GANs) have demonstrated accepted performances in image super-resolution owing to their powerful generative capabilities. However, remote sensing images have complex feature types, which largely limits the performances of GAN-based SR methods for real satellite images. To address this issue, an attention mechanism and a multi-scale structure are introduced into the generator of the GAN network, and a multi-scale attention GAN(MSAGAN) is constructed in this study. We sequentially arrange the channel attention module and the spatial attention module after the multi-scale structure to emphasize important information, suppress unimportant information details, improve the model’s performance. Furthermore, we add residual connections and dense blocks to further enhance the performance of the generative network by increasing its depth. We compared to other existing deep learning-based SR methods, our proposed MSAGAN algorithm performed better in generating high spatial satellite images.

The reconstruction of high-resolution (HR) remote sensing images (RSIs) from low-resolution (LR) counterparts is a critical task in remote sensing image super-resolution (RSISR). Recent advancements in convolutional neural networks (CNNs) and Transformers have significantly improved RSISR performance due to their capabilities in local feature extraction and global modeling. However, several limitations remain, including the underutilization of multi-scale features in RSIs, the limited receptive field of Swin Transformer’s window self-attention (WSA), and the computational complexity of existing methods. To address these issues, this paper introduces the NGSTGAN model, which employs an N-Gram Swin Transformer as the generator and a multi-attention U-Net as the discriminator. The discriminator enhances attention to multi-scale key features through the addition of channel, spatial, and pixel attention (CSPA) modules, while the generator utilizes an improved shallow feature extraction (ISFE) module to extract multi-scale and multi-directional features, enhancing the capture of complex textures and details. The N-Gram concept is introduced to expand the receptive field of Swin Transformer, and sliding window self-attention (S-WSA) is employed to facilitate interaction between neighboring windows. Additionally, channel-reducing group convolution (CRGC) is used to reduce the number of parameters and computational complexity. A cross-sensor multispectral dataset combining Landsat-8 (L8) and Sentinel-2 (S2) is constructed for the resolution enhancement of L8’s blue (B), green (G), red (R), and near-infrared (NIR) bands from 30 m to 10 m. Experiments show that NGSTGAN outperforms the state-of-the-art (SOTA) method, achieving improvements of 0.5180 dB in the peak signal-to-noise ratio (PSNR) and 0.0153 in the structural similarity index measure (SSIM) over the second best method, offering a more effective solution to the task.

Small object detection in unmanned aerial vehicle (UAV) remote sensing images remains challenging due to large-scale variations, dense object distributions, and complex background interference. Although Transformer-based detectors have improved global context modeling for remote sensing object detection, many existing designs still rely on spatial geometric relationships and conventional cross-level fusion, which may limit the aggregation of spatially scattered small-object features and introduce background interference. To address these issues, this paper proposes a Multi-granularity Prompt and Intensity Guidance Detection Transformer (MPI-DETR), an efficient end-to-end Transformer-based detector for small-object detection in UAV remote sensing images. MPI-DETR consists of three key components: a Dual-Stream Ranked Self-Attention (DRSA) module for intensity-ordered global feature aggregation, a Bilateral Tanh Gating and Cosine Attention Feature Alignment Module (BTC-FAM) for noise-resistant cross-level alignment, and a Prompt-driven Multi-granularity Fusion (PMGF) module for enhancing weak small-object details. Experiments on AI-TOD, DIOR, and NWPU VHR-10 demonstrate that MPI-DETR achieves $A{P}_{50}$ scores of $43.8\%$, $87.5\%$, and $92.5\%$, respectively. Compared with the RT-DETR-R18 baseline, MPI-DETR improves $A{P}_{50}$ by 3.0, 1.0, and 3.9 percentage points on the three datasets, respectively, and increases $A{P}_{\mathrm{S}}$ by 2.9, 2.9, and 11.6 percentage points. It also surpasses the strongest compared models by 1.7, 1.0, and 1.3 percentage points in $A{P}_{50}$ on AI-TOD, DIOR, and NWPU VHR-10, respectively. These results indicate that MPI-DETR provides a robust and efficient solution for small-object perception in complex UAV remote sensing applications, especially in scenes with dense targets, background interference, and limited computational resources.

With the advancement of remote sensing technology, image acquisition has become more convenient and the amount of information captured has significantly increased, playing a vital role in numerous fields. However, cloud cover often results in missing image data, severely affecting data usability. In recent years, although deep learning methods have made progress in cloud removal tasks, the complexity of modeling multispectral band relationships and the scarcity of paired data remain major challenges. To address this, this paper proposes a two-stage codebook fusion vector-quantized generative adversarial network (TCF-VQ GAN) and a training framework. The first stage employs synthetic aperture radar (SAR), MODIS, and cloud-free data for unsupervised training; the second stage performs fusion fine-tuning using SAR and MODIS on paired cloudy/cloud-free data. The model incorporates a space-channel jointed gated convolution (SCGC) module to model irregular cloud cover and combines channel attention for band selection, while a dynamically weighted wavelet alignment loss function (DW²A) is designed to enhance multiscale feature representation. Experiments on the SEN12MS-CR and SMILE-CR datasets demonstrate that the proposed method outperforms existing methods across all metrics: on SEN12MS-CR, PSNR is 31.0397 and SAM is 4.7243; they are 33.5191 and 2.1663, respectively, on SMILE-CR. Furthermore, under fixed paired data conditions, simply adding auxiliary and cloud-free data further improves performance, validating the method’s effectiveness in data-scarce scenarios.

Objectives: The rapid advancement of Multimodal Large Language Models (MLLMs) has significantly enhanced their reasoning capabilities, enabling a wide range of intelligent applications. However, these advancements also raise critical concerns regarding privacy and ethics. MLLMs are now capable of inferring the geographic location of images -- such as those shared on social media or captured from street views -- based solely on visual content, thereby posing serious risks of privacy invasion, including doxxing, surveillance, and other security threats. Methods: This study provides a comprehensive analysis of existing geolocation techniques based on MLLMs. It systematically reviews relevant litera-ture and evaluates the performance of state-of-the-art visual reasoning models on geolocation tasks, particularly in identifying the origins of street view imagery. Results: Empirical evaluation reveals that the most advanced visual large models can successfully localize the origin of street-level imagery with up to $49\%$ accuracy within a 1-kilometer radius. This performance underscores the models' powerful capacity to extract and utilize fine-grained geographic cues from visual data. Conclusions: Building on these findings, the study identifies key visual elements that contribute to suc-cessful geolocation, such as text, architectural styles, and environmental features. Furthermore, it discusses the potential privacy implications associated with MLLM-enabled geolocation and discuss several technical and policy-based coun-termeasures to mitigate associated risks. Our code and dataset are available at https://github.com/zxyl1003/MLLM-Geolocation-Evaluation.