Cloud contamination is a common issue that severely reduces the quality of optical satellite images in remote sensing fields. With the rapid development of deep learning technology, cloud contamination is expected to be addressed. In this paper, we propose Denoising Diffusion Probabilistic Model-Cloud Removal (DDPM-CR), a novel cloud removal network that can effectively remove both thin and thick clouds in optical image scenes. Our network leverages the denoising diffusion probabilistic model (DDPM) architecture to integrate both clouded optical and auxiliary SAR images as input to extract DDPM features, providing significant information for missing information retrieval. Additionally, we propose a cloud removal head adopting the DDPM features with an attention mechanism at multiple scales to remove clouds. To achieve better network performance, we propose a cloud-oriented loss that considers both high- and low-frequency image information as well as cloud regions in the training procedure. Our ablation and comparative experiments demonstrate that the DDPM-CR network outperforms other methods under various cloud conditions, achieving better visual effects and accuracy metrics (MAE = 0.0229, RMSE = 0.0268, PSNR = 31.7712, and SSIM = 0.9033). These results suggest that the DDPM-CR network is a promising solution for retrieving missing information in either thin or thick cloud-covered regions, especially when using auxiliary information such as SAR data.

Change detection with very high resolution (VHR) satellite images is of great application values when evaluating and monitoring land use changes. However, intrinsic complexity of satellite images will introduce more difficulties to change detection tasks. In this study, a new change detection method is proposed by combining multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) with stacked convolutional auto-encoder (SCAE) features to improve change detection capabilities with VHR satellite images. First, the multi-scale SLIC-based image segmentation is performed on multi-temporal images to generate segment objects while keeping their edge information as much as possible. Second, the convolutional layers in a CNN architecture are used to generate change map, then, an SCAE feature-based classification procedure is performed to generate “from-to” change information. Finally, a Bayesian information criterion is used to optimize the results of change detection. In this study, the experiments carried out reveal that the multi-scale SLIC image segmentation algorithm affects the integrity of change regions; CNN features have an effect on the consistency of change regions; and SCAE features influence the performance of support vector machine (SVM) classifiers. And, features extracted from the architectures enhance the ability of information extraction from ground objects. Comparison results also show the superiority to other change detection methods.

Accurate island road centerlines are important to tourism planning, resource development, and other applications. However, high-resolution island imaging is highly detailed with complex features, which increases the difficulty of road centerline extraction. To address the problem, we propose a method based on a multiscale united feature, which can improve the extraction of road centerlines in high-resolution island images. First, a biological simulation saliency algorithm is used to determine the island extent. Then, the spectral, geometric, and texture features of roads in high-resolution images are fully utilized, and a multiscale united feature is generated by the effective combination of the features to enhance road characteristics. Road regions are extracted by the implementation of the united feature. Finally, with the advantage of reconstruction ability within strong noise, a tensor voting algorithm is applied to refine the road regions, and then, the road centerline is extracted. The following conclusions are obtained by comparing the proposed method with three state-of-the-art methods: For a study area in which roads present clear characteristics and are less covered, the proposed method exhibits better road centerline extraction performance, and its completeness, correct rate, and quality index are better than the methods to which it was compared; the same was true for a study area with complicated road distribution. The proposed method can be applied to road centerline extraction of high-resolution island images.

Lithological identification of outcrops in complex geological settings plays a crucial role in hydrocarbon exploration and geological modeling. To address the limitations of traditional field surveys, such as low efficiency and high risk, we proposed an intelligent lithology recognition method, SG-RFGeo, for terrestrial laser scanning (TLS) outcrop point clouds, which integrates spectral and geometric features. The workflow involves several key steps. First, lithological recognition units are created through regular grid segmentation. From these units, spectral reflectance statistics (e.g., mean, standard deviation, kurtosis, and other related metrics), and geometric morphological features (e.g., surface variation rate, curvature, planarity, among others) are extracted. Next, a double-layer random forest model is employed for lithology identification. In the shallow layer, the Gini index is used to select relevant features for a coarse classification of vegetation, conglomerate, and mud–sandstone. The deep-layer module applies an optimized feature set to further classify thinly interbedded sandstone and mudstone. Geological prior knowledge, such as stratigraphic attitudes, is incorporated to spatially constrain and post-process the classification results, enhancing their geological plausibility. The method was tested on a TLS dataset from the Yueyawan outcrop of the Qingshuihe Formation, located on the southern margin of the Junggar Basin in China. Results demonstrate that the integration of spectral and geometric features significantly improves classification performance, with the Macro F1-score increasing from 0.65 (with single-feature input) to 0.82. Further, post-processing with stratigraphic constraints boosts the overall classification accuracy to 93%, outperforming SVM (59.2%), XGBoost (67.8%), and PointNet (75.3%). These findings demonstrate that integrating multi-source features and geological prior constraints effectively addresses the challenges of lithological identification in complex outcrops, providing a novel approach for high-precision geological modeling and exploration.

NDVI is an important parameter for environmental assessment and precision agriculture that well-describes the status of vegetation. Nevertheless, the clouds in optical images often result in the absence of NDVI information at key growth stages. The integration of SAR and optical image features will likely address this issue. Although the mapping of different data sources is complex, the prosperity of deep learning technology provides an alternative approach. In this study, the double-attention RNN architecture based on the recurrent neural network (RNN) and attention mechanism is proposed to retrieve NDVI data of cloudy regions. Overall, the NDVI is retrieved by the proposed model from two aspects: the temporal domain and the pixel neighbor domain. The performance of the double-attention RNN is validated through different cloud coverage conditions, input ablation, and comparative experiments with various methods. The results conclude that a high retrieval accuracy is guaranteed by the proposed model, even under high cloud coverage conditions (R² = 0.856, RMSE = 0.124). Using SAR images independently results in poor NDVI retrieval results (R² = 0.728, RMSE = 0.141) with considerable artifacts, which need to be addressed with auxiliary data, such as IDM features. Temporal and pixel neighbor features play an important role in improving the accuracy of NDVI retrieval (R² = 0.894, RMSE = 0.096). For the missing values of NDVI data caused by cloud coverage, the double-attention RNN proposed in this study provides a potential solution for information recovery.