Clouds in optical remote sensing images (ORSI) significantly limit image utilization. Traditional cloud removal methods using single or multi-temporal data sources struggle to ensure reliable reconstruction for thick cloud areas. Synthetic Aperture Radar (SAR) images are increasingly used to recover information obscured by clouds, but their performance in cloud-obscured regions is unstable. Therefore, an adaptive cloud removal network for remote sensing images, named DG2-TCR, is proposed based on SAR-driven dual-flow fusion guidance (DFG). DG2-TCR uses SAR and ORSI to construct DFG, including local spatial-spectral feature reconstruction (LSSFR) flow and global texture feature compensation (GTFC). LSSFR, driven by ORSI and SAR, efficiently extracts useful features in non-cloud areas and focuses on local information reconstruction using the designed spatial-spectral features inference reconstruction block (SSIRB). Based on SAR images, GTFC guides the compensation of global texture information. DFG can adaptively extract features and reconstruct missing information from local and global scales. The public SEN12MS-CR-TS dataset is divided into four sub-datasets with different coverage to evaluate the recovering capability in varying clouds. Experiments show that the PSNR, SSIM, RMSE, FID, and NCC indicator values on four sub-datasets and the SIMLE-CR dataset are better than the seven comparison methods. Furthermore, the ablation experiments show that the generalization and robustness of this proposed method on images with different cloud coverage are better than other comparison methods. Therefore, DG2-TCR can reliably recover information on cloud occlusions with various coverage and thickness, which is significant for cloud removal in practical applications.

With the rapid development of hyperspectral image classification (HSIC) technology, its applications in geological exploration and environmental monitoring have become increasingly prominent. Recently, Mamba has garnered significant attention owing to its outstanding performance in long-range sequence modeling and linear computational complexity. However, Mamba still exhibits significant limitations in HSIC: first, it does not fully consider the hierarchical spatial-contextual representation and nonlinear spectral interactions in hyperspectral images; second, its sequential processing approach leads to the loss of spatial structural information and feature redundancy. In response, this study proposes a structure-enhanced spatial–spectral dynamic gating Mamba (SEDGM) that leverages the collaborative design of spatial and spectral gating Mamba mechanisms to extract and exploit key regional features of hyperspectral data. The spatial branch employs Hierarchical Gating Mamba to capture multi-directional pixel sequences and extract the hierarchical spatial features and their intrinsic relationships. In contrast, the spectral branch utilizes a Random Shuffled Gating Mamba to disrupt the fixed order of traditional spectral sequences and capture higher-order spectral couplings, effectively characterizing the cooperative variation patterns of spectral features. Both branches employ a dynamic gating mechanism that weights features based on sequence centrality, dynamically activating feature sequences. Additionally, shape-specific offset-aware attention is incorporated into each branch to enhance the structured features that were lacking in the Mamba sequences. Finally, a Spectral-Oriented Feature Review Module is incorporated to achieve dynamic feature fusion and optimized refinement. Experiments were conducted on four large-scale benchmark HSI datasets, with SEDGM achieving significant improvements in classification performance, validating the effectiveness of this approach in hyperspectral image classification tasks. The code is available at https://github.com/shuai2023-hash/SEDGM.

The global cloud detection of high-resolution remote sensing images (HRSI) is crucial for acquiring high-quality imagery and optimizing data utilization. Traditional cloud detection models, which rely on limited samples and fully supervised learning, struggle to adapt to cross-temporal and cross-spatial domains. While current unsupervised domain adaptation (UDA) methods improve performance in cross-domain cloud detection to some extent, generating high-quality, reliable pseudo-labels remains a significant challenge for global cloud detection. Therefore, this paper proposes a progressive pseudo-label learning for cross-domain cloud detection in high-resolution remote sensing (PTDA). Firstly, we propose an online domain-invariant feature guided pseudo-label generation (OPLG) strategy and learning intra-domain unaligned features (LIUF), which effectively integrate domain-invariant features and intra-domain semantics to generate high-quality pseudo-labels at the feature level. LIUF then refines the pseudo-label quality at the pixel level. Secondly, during the model training, pseudo-label constrained intra-domain feature mining loss(PCIF Loss) is designed to suppress noisy semantic information within the domain, the hole effect of thick/thin clouds, and the noise interference of the contour boundary. Four cloud detection datasets, including MS Cloud (MS), HRC WHU Cloud (WHU), 95 Cloud(95), and WHUS2-CD+(S2), are grouped into three cross-domain tests, MS2WHU, MS2S2, and WHU295. Our approach achieved the best performance with mIoU 63.99%, 58.14%, 58.82%, and OA 80.03%, 79.36%, 80.49%, respectively. The experimental results show that the proposed method outperforms seven state-of-art cross-domain comparison methods. Thus, our method has important application value for cross-domain cloud detection. The available code can be downloaded from https://github.com/gasking/PTDA.

Deep learning-based disparity estimation methods have demonstrated significant potential in optical satellite stereo image applications. However, learning-based methods remain susceptible to domain shifts caused by spatiotemporal variations and stereo-sensor heterogeneity. To address these challenges, we propose a Hierarchical Domain Adaptation Disparity Estimation framework (HDADE) for optical satellite stereo images. HDADE was structured with a four-stage technique pipeline to improve the training data quality and diversity, explicitly align the spectral and stereo distribution, implicitly enhance the robustness of feature extraction and matching, directly facilitate feature alignment with the target domain. This hierarchical framework systematically mitigates disparity estimation accuracy degradation in cross-domain scenarios. Cross-spatiotemporal and cross-payload generalization experiments were conducted based on the WHU_Stereo and US3D datasets. The experimental results show that HDADE significantly outperformed other advanced methods and possessed plug-and-play versatility. Notably, greater domain shift scene transfer experiments indicated that, with limited annotation data, HDADE has the potential for large-scale automatic applications.

Building height is one of the important data for understanding urban development and changes. Building height estimation using a digital surface model (DSM) based on the difference between the roof elevation and the ground elevation of the building is commonly utilized. However, owing to the limitations of existing DSM techniques, invalid values may exist in the DSM. Existing DSM-based methods for estimating building heights typically use the interpolated DSM; however, when there are many invalid values, there may be errors in the interpolation results, which can mislead the selection of ground elevation values. Therefore, we propose a building-height extraction method that combines an optimal selection region and a multiindex evaluation mechanism to reduce the impact of invalid values and complex terrains. First, the optimal area for the ground elevation search was obtained based on the spatial relationship between the target building and surrounding buildings. Second, a joint multiindicator weighted evaluation mechanism was used to obtain the optimal ground elevation value. Finally, the building height was determined based on the difference between the roof and the ground elevations. Four build-up areas were used to test the effectiveness of the proposed method. The results exhibit high accuracy in complex areas with variable ground elevations, with a mean absolute error (MAE) of 1.16 m in building height. In areas with many invalid values and large shadow coverage of the surface areas, the MAE in building height is 0.92 m. Additionally, we verified the accuracy of the ground elevation estimated after interpolation. It is evident that the performance of the original DSM is satisfactory, with a high tolerance for input data and ability to be used in different building scenarios, providing new ideas for studying building height estimates.

Cloud detection in high-resolution remote sensing images (HRSI) is widely applied to cross-spatiotemporal domains with various scenarios change. However, cloud detection semantic segmentation models based on limited samples cannot ensure the consistency of data distribution between the source domain (SD) and the target domain (TD), resulting in a decrease in cross-domain segmentation accuracy and robust ability. Therefore, this paper proposed a two-stage domain adaptation based on the image and feature levels (TDAIF) cloud detection framework. TDAIF designs a pseudo-target domain data generator (PTDDG) at the image level to fuse the SD foreground and TD background information effectively, assisting the model in mining invariant semantic knowledge of the TD. Then, a domain discriminator and self-ensembling joint (DDSEJ) framework is explored at the feature level to implicitly handle the alignment of global features and the optimization of decision boundaries-local features. TDAIF ultimately weakens the impact of image radiation diversity and scale divergence and improves the adaptive processing capabilities for cross-spatiotemporal data. Horizontal and internal comparative experiments on TDAIF were conducted on three domain transfer data. Experimental results show that TDAIF dramatically reduces the network accuracy loss in cross-domain. Compared with CycleGAN and AdaptSegNet, the IoU is improved by about 30%. TDAIF performs better than state-of-the-art computational visual domain adaptation methods, indicating that hierarchical data alignment from the image to the feature level is very effective.

In the field of photogrammetry and remote sensing, the task of satellite stereo image disparity estimation (SSIDE) has long been recognized as both challenging and important. Currently, deep-learning methods are gaining prominence in the SSIDE domain. However, the inconsistency between stereo images and ground truth makes the fine training and accurate inference of SSIDE networks extremely difficult. Furthermore, the existence of textureless and repeated texture areas in satellite images complicates the execution of end-to-end SSIDE networks, especially in areas with variable illumination conditions. In this study, a sparse matching point-guided disparity estimation (SMP-DE) general framework was introduced to address such concerns. SMP-DE employed sparse matching point-guided data evaluation and distillation (SMP-DED) for fault-tolerant training and ensuring unbiased guidance training as well as reliable reasoning. In addition, SMP-DE executed optimization for the disparity estimation network across various feature spaces by integrating sparse matching point-guided feature contrastive registration (SMP-FCR) and matching cost uniqueness constraint (MCUC) modules. Therefore, SMP-DE can mine homogenous features and model low-entropy matching costs in challenging regions. Experimental results demonstrated that SMP-DE has outstanding disparity estimation accuracy and generalization compared with other advanced methods. Furthermore, the proposed SMP-DED exhibited excellent flexibility and generality, since it can be combined with various disparity estimating networks, giving the networks an accuracy boost on a range of datasets. In summary, SMP-DE provides a novel perspective for end-to-end SSIDE research.

Leaf area index (LAI) is an important indicator for monitoring vegetation growth and estimating crop yields. The empirical-based model using vegetation indices (VIs) is an effective method for LAI estimation at the regional scale. However, due to the complexity of canopy radiation interaction processes, the leaf chlorophyll content (Cab) and saturation effects on canopy reflectance restrict the accuracy of VI-based LAI retrieval. To address these limitations, we propose a novel chlorophyll-insensitive vegetation index (CIVI) using red, red-edge and near-infrared bands to improve regional LAI mapping. The CIVI was developed based on the sensitivity analysis of red-edge band reflectance to LAI andCabusing the simulation dataset from the PROSAIL model. Then, the performance of CIVI was carefully evaluated from two aspects: the sensitivity of VI to LAI and other parameters, and the accuracy of LAI estimates using different VIs over homogeneous (cropland and grassland) and non-homogeneous (forest) biome canopies. The results suggested that CIVI can capture LAI variations well while remaining insensitive toCabvariations. Additionally, the sensitivity of CIVI to other vegetation biochemical and biophysical parameters did not increase significantly compared to that of other VIs. Furthermore, CIVI exhibited the best performance of LAI retrievals over both homogeneous (R2=0.938, RMSE=0.447 and rRMSE=21.3%) and non-homogenous (R2=0.635, RMSE=0.693 and rRMSE=14.0%) canopies among all selected VIs, especially for the high LAI. Our results indicated that the developed CIVI incorporating red-edge bands with a suitable formula can effectively reduce theCaband saturation effects, which is promising for improving VI-based LAI estimation.