Epipolarity is the foundation of epipolar resampling, which is used to eliminate the vertical disparity between stereo pairs in stereo matching. To analyze the epipolarity of pushbroom satellite images, this paper compares three methods based on the rational function model (RFM): the projection trajectory method (PTM), the piecewise projection trajectory method (PPTM) and our extended projection trajectory method (EPTM). To evaluate the quality of epipolar curves, we defined the deviation coefficient as a metric to evaluate the bending degree of epipolar curves. We also defined the maximum deviation coefficient of an image that can be used to determinate the tile size in multiview satellite image 3D reconstruction based on image dividing. Comparison experiments have been carried out with pushbroom satellite images using these three methods. Experimental results show that our EPTM is more convenient and practical. It only needs the forward form of the RFM to analyze the epipolarity and can be used in the epipolarity analysis of a single image. By projecting straight lines in the ground space into the image space, the EPTM can be used to perform comprehensive epipolarity analysis for pushbroom satellite images. In addition, the EPTM can be used to calculate the maximum deviation coefficient of an image that the PTM and the PPTM cannot calculate, which is important in 3D reconstruction using multiview satellite images.

As the basis of stereo observation, epipolar resampling is a critical technology for 3D reconstruction. With the development of the 3D reconstruction using multi-view satellite images, the epipolar resampling of the multi-view satellite images has become a new challenging problem. In this paper, we propose an epipolar resampling method for multi-view high resolution satellite images based on block. Firstly, we establish the relationship between the vertical parallax and image block size for the epipolar resampling based on block. Using this relationship, we can choose the block size to limit the maximum vertical parallax in epipolar resampling. Secondly, we use the rational function model (RFM) to generate virtual corresponding points for fundamental matrix estimation, so image pairs with large stereo angles and poor consistency in multi-view satellite images which are difficult to extract feature matching point can be rectified. Experiments with multi-view satellite images demonstrated the universality and effectiveness of the proposed method. All the image pairs in the multi-view satellite image dataset were rectified successfully. The average epipolar resampling error is only 0.25 pixels when we rectify the multi-view satellite images acquired by WorldView-3 with the block size of 3000*3000 pixels, significantly less than the similar method.

Aiming at the problems of insufficient network fusion and low detection efficiency in current object recognition using RGB-D images, a recognition algorithm based on the medium-level layer-by-layer fusion of dual-channel networks is proposed. First of all, the RGB and Depth networks are trained with ten labelled RGB-D indoor objects respectively, and then determine the fusion coefficients according to the identify accuracy of two types networks. Finally, two kinds of features are merged in convolutional layers step by step to obtain the final weights. By testing on the challenging NYU Depth v2 dataset, we found that the recognition accuracy of our method is 92.85%, and average detection time is 61.03ms per image. Through comparison experiments, we got the conclusion that average accuracy of the RGB-D layer-by-layer fusion network is 5.22% higher than that of the RGB network.

Large-scale three-dimensional (3D) reconstruction from multi-view images is used to generate 3D mesh surfaces, which are usually built for urban areas and are widely applied in many research hotspots, such as smart cities. Their simplification is a significant step for 3D roaming, pattern recognition, and other research fields. The simplification quality has been assessed in several studies. On the one hand, almost all studies on surface simplification have measured simplification errors using the surface comparison tool Metro, which does not preserve sufficient detail. On the other hand, the reconstruction precision of urban surfaces varies as a result of homogeneity or heterogeneity. Therefore, it is difficult to assess simplification quality without surface classification. These difficulties are addressed in this study by first classifying urban surfaces into planar surfaces, detailed surfaces, and urban frameworks according to the simplification errors of different types of surfaces and then measuring these errors after sampling. A series of assessment indexes are also provided to contribute to the advancement of simplification algorithms.

This paper proposes a novel multi-view 3D dense matching method for large-scale aerial images using a divide-and-conquer scheme. Firstly, the original sparse reconstruction result is divided into several sub-clusters based on the relationship of the camera projection location, and the bounding box of each sub-cluster is obtained. An efficient patch-based stereo matching strategy is then performed, followed by multi-photo geometrically constrained (MPGC) matching optimization to generate a depth map for each image in the sub-clusters, with the limited patch expansion range according to the bounding box of the sub-cluster. Redundancy points are removed by enhanced depth consistency in different views, which contributes to high-accuracy depth map fusion. Lastly, the dense points of each sub-cluster can be easily grouped together due to the determined boundaries. This method can be easily parallelized at the image level, and is highly suitable for the large-scale reconstruction of aerial images. The experimental results show that the proposed method has advantages over the state-of-the-art method in terms of reconstruction accuracy and efficiency.