Clouds and accompanying shadows, which exist in optical remote sensing images with high possibility, can degrade or even completely occlude certain ground-cover information in images, limiting their applicabilities for Earth observation, change detection, or land-cover classification. In this paper, we aim to deal with cloud contamination problems with the objective of generating cloud-removed remote sensing images. Inspired by low-rank representation together with sparsity constraints, we propose a coarse-to-fine framework for cloud removal in the remote sensing image sequence. Leveraging on group-sparsity constraint, we first decompose the observed cloud image sequence of the same area into the low-rank component, group-sparse outliers, and sparse noise, corresponding to cloud-free land-covers, clouds (and accompanying shadows), and noise respectively. Subsequently, a discriminative robust principal component analysis (RPCA) algorithm is utilized to assign aggressive penalizing weights to the initially detected cloud pixels to facilitate cloud removal and scene restoration. Moreover, we incorporate geometrical transformation into a low-rank model to address the misalignment of the image sequence. Significantly superior to conventional cloud-removal methods, neither cloud-free reference image(s) nor additional operations of cloud and shadow detection are required in our method. Extensive experiments on both simulated data and real data demonstrate that our method works effectively, outperforming many state-of-the-art approaches.

Optical remote sensing has emerged as a crucial technique for earth observation. However, interference of clouds and fog can adversely affect the spatial and spectral information of the images, presenting significant challenges in interpreting remote sensing data and limiting its availability. Moreover, existing methods for addressing the issue of cloud occlusion primarily rely on either physical models or neural networks, lacking a comprehensive integration of the advantages offered by both approaches. So, we propose an end-to-end Asymmetric Unet (AU-Net) dehazing network based on an atmospheric scattering model, which realizes the joint optimization of physical parameters. Firstly, we utilize the Unet to estimate the relatively constant atmospheric light A . Simultaneously, AU-Net based on dark channel prior guidance is used to estimate more complex transmission map T . Next, the atmospheric scattering model is used to deduce the rough dehazing image J based on A and T . Finally, we refine J using AU-Net once again. More specifically, AU-Net incorporates a plug-and-play attention module, namely, Self-Attention with Depth (SAD) and Channel Attention (CA). SAD enhances the fusion of deep semantic information and shallow detail features by expanding the network’s depth, thereby effectively alleviating the dependence of the network on long-distance information. On the other hand, CA addresses the limitations of SAD in terms of channel information. To validate the effectiveness and applicability of proposed dehazing network and attention module, we have achieved state-of-the-art performance and visually appealing results on remote sensing and natural image datasets. The code will be made public on https://github.com/Dudujia160918/Dehazing_Network_AU-Net.

Limited by the noise, missing data and varying sampling density of the point clouds, planar primitives are prone to be lost during plane segmentation, leading to topology errors when reconstructing complex building models. In this paper, a pipeline to recover the broken topology of planar primitives (TopoLAP) is proposed to reconstruct level of details 3 (LoD3) models. Firstly, planar primitives are segmented from the incomplete point clouds and feature lines are detected both from point clouds and images. Secondly, the structural contours of each plane segment are reconstructed by subset selection from intersections of these feature lines. Subsequently, missing planes are recovered by plane deduction according to the relationships between linear and planar primitives. Finally, the manifold and watertight polyhedral building models are reconstructed based on the optimized PolyFit framework. Experimental results demonstrate that the proposed pipeline can handle partial incomplete point clouds and reconstruct the LoD3 models of complex buildings automatically. A comparative analysis indicates that the proposed method performs better to preserve sharp edges and achieves a higher fitness and correction rate than rooftop-based modeling and the original PolyFit algorithm.

Nadir viewing satellite image is an effective data source to generate orthomosaics. Because of the georeferencing error of satellite images, block adjustment is the first step of orthomosaic generation over a large area. However, the geometric relationship of the neighboring orbits of the nadir viewing images is not rigid enough. This paper proposes a new rational function model (RFM) block adjustment approach that constrains the tie point elevation to enhance the relative geometric rigidity. By interpolating the elevations of tie points in a digital elevation model (DEM) and estimating the a priori errors of the interpolated elevations, better overall relative accuracy is obtained, and the local optimal solution problem is avoided. By constraining the adjusted model parameters according to the a priori error of RFMs, block adjustment without ground control point (GCP) is performed. By optimal initializing the object-space positions of tie points with multi-backprojection method, the needed iteration times of block adjustment are reduced. The proposed approach is investigated with 46 Ziyuan-3 sensor-corrected images, a 1:50 000 scale DEM, and 586 GCPs. Compared with Teo's approach that constrains the horizontal coordinates and elevations of tie points, the approach in this paper converges much faster when the GCPs are sparse, and meanwhile, the absolute and relative accuracy of the two approaches are almost the same. The result of block adjustment with only four GCPs shows that no accuracy degeneration occurred in the test area and the root-mean-square error of independent check point reaches about 1.5 ground resolutions. Different DEMs and number of tie points are used to investigate whether the block adjustment result is influenced by these factors. The results show that better DEM accuracy and denser tie points do improve the accuracy when the images have large side-sway angles. The proposed approach is also tested with 5118 IKONOS-2 images that cover the southern Europe without GCP. The result shows that the relative mosaicking accuracy is much better than that of Grodecki's approach.