In the field of remote sensing image denoising, the current mainstream methods usually only consider using clean or noisy images to guide the network in the training phase. Most of them only apply to specific types of noise, and the denoising effect is not satisfactory enough, with problems such as artifacts and noise residues. In this paper, we endeavor to deal with a wide range of noise types, preserving as much detailed information in the image as possible and aiming to address the relevant limitations. Inspired by contrastive learning, we propose a remote sensing image denoising framework based on contrastive learning, named RSID-CR, which constructs positive and negative sample pairs between clean, noisy, and denoised images. Then, we construct a joint loss function consisting of reconstruction loss and contrastive regularization as a guide signal to train the denoising network, such that the denoised image is pushed closer to the clean image and farther away from the noisy image in the feature space. We conduct extensive experiments on two public datasets for five types of noise often present in remote sensing images. Additionally, we validate our method using two real noisy remote sensing datasets. The experimental results indicate that our proposed method achieves satisfactory outcomes.

Fugitive dust is an important source of total suspended particulate matter in urban ambient air. The existing segmentation methods for dust sources face challenges in distinguishing key and secondary features, and they exhibit poor segmentation at the image edge. To address these issues, this paper proposes the Dust Source U-Net (DSU-Net), enhancing the U-Net model by incorporating VGG16 for feature extraction, and integrating the shuffle attention module into the jump connection branch to enhance feature acquisition. Furthermore, we combine Dice Loss, Focal Loss, and Activate Boundary Loss to improve the boundary extraction accuracy and reduce the loss oscillation. To evaluate the effectiveness of our model, we selected Jingmen City, Jingzhou City, and Yichang City in Hubei Province as the experimental area and established two dust source datasets from 0.5 m high-resolution remote sensing imagery acquired by the Jilin-1 satellite. Our created datasets include dataset HDSD-A for dust source segmentation and dataset HDSD-B for distinguishing the dust control measures. Comparative analyses of our proposed model with other typical segmentation models demonstrated that our proposed DSU-Net has the best detection performance, achieving a mIoU of 93% on dataset HDSD-A and 92% on dataset HDSD-B. In addition, we verified that it can be successfully applied to detect dust sources in urban areas.

In photovoltaic remote sensing image segmentation tasks, fully supervised methods can achieve high accuracy. However, the high cost of pixel-level annotation significantly limits their scalability in large-scale scenarios. To overcome this annotation bottleneck, this paper proposes a zero-shot cross-modal segmentation framework based on the visual-language pre-trained foundation model (CLIP). This approach harnesses CLIP’s cross-modal knowledge transfer capabilities to achieve precise extraction of photovoltaic targets without requiring any downstream training. This paper first introduces the Layer-wise Augmented Residual Attention (LARA) mechanism to enhance fine-grained detail representation in the feature space. Subsequently, a Cross-modal Semantic Attribution Module (CMSA) is designed to generate precise activation maps by leveraging image-text alignment gradient information. Finally, the Confidence-Aware Refinement Strategy (CARS) replaces the conventional training-based denoising process, directly producing high-quality binary segmentation masks through adaptive thresholding. Comparative experiments were conducted to evaluate the proposed method against various baselines using several public datasets with varying resolutions in Jiangsu Province including Unmanned Aerial Vehicles imagery, Beijing-2, Gaofen-2, and a self-created Sentinel-2 imagery covering multiple countries. Notably, the proposed method achieved an IoU of 70.3% on the Gaofen-2 PV03 dataset with a spatial resolution of approximately 0.3 m and 50.8% on the self-created Sentinel-2 PV_Sentinel-2 dataset with a spatial resolution of 10 m. Experimental results demonstrate that our proposed approach maintains excellent cross-domain generalisation capabilities while reducing annotation costs, thereby providing an efficient and viable technical pathway for the automated monitoring of large-scale photovoltaic facilities.