The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.

Global color consistency correction for multiview images in three-dimensional (3-D) reconstruction is an important problem. The color differences between the images will affect the result of dense matching, thereby reducing the geometric accuracy of the mesh model. Moreover, it will also affect the result of texture mapping, causing color differences in the textured model. The color correction method based on global optimization is mainly used to solve this problem. And existing methods usually use sparse matching points as the color correspondences, but the correction results are not accurate enough as a result of the sparsity of the points. Besides, their efficiency of solving large-scale images globally is low. This article proposes a novel color correction method to eliminate the color differences between large-scale multiview images effectively. The core idea of our method is to group images by graph partition algorithm, and then perform intragroup correction and intergroup correction in sequence. First, for each pair of images, we calculate the reliable matching regions around the sparse points as the color correspondences according to the local homography principle. Compared with sparse matching points, our strategy can achieve more accurate color correction results. Next, for large-scale images, we partition them into many groups. For each group of images, the correction parameters are solved to eliminate the color differences of the images included in the group. Finally, we eliminate the color differences between groups by intergroup correction to achieve overall color consistency. Experimental results on typical datasets demonstrate that the proposed method is better than the current representative methods. The proposed method shows better color consistency in the extreme cases, and also exhibits higher computational efficiency on large-scale image sets.

Color consistency optimization for multiple images is a challenging task in remote sensing and computer vision. To ensure that the visual quality of corrected images is satisfactory, not only should the color discrepancies between multiple images be invisible but also the contrast of individual images should be visually appealing. Most color correction approaches focus on eliminating drastic color discrepancies, but ignore the problem of image contrast enhancement. Some color correction approaches even tend to degrade the image contrast to ensure that the image tones are consistent especially when the contrast of input images is low. To solve this problem, we present a contrast-aware color consistency correction approach in this article. We attempt to eliminate the drastic color differences and enhance the contrast of input images simultaneously. We creatively integrate the problems of color consistency correction and image contrast enhancement into the same global energy optimization framework, and we also design a special cost function to minimize the color discrepancies and enhance the image contrast using the original color information. Thus, although the contrast of input images is low, our approach can still generate the corrected images with consistent tones and visually appealing contrast. At last, we select several challenging datasets to evaluate our approach. The experimental results visually and quantitatively demonstrate the effectiveness and superiority of the proposed contrast-aware color consistency correction approach. The results also demonstrate that our approach significantly outperforms the existing approaches, especially when the contrast of the input images is low.

Orthoimage mosaicking aims to make the seamline bypass obvious objects to produce seamless digital orthophoto maps. Nevertheless, stitching artifacts may still occur when obvious objects are inevitably crossed by the seamline or when the orthoimages are not accurately aligned in geometry. Image warping is necessary to correct the geometric misalignments. Existing image warping approaches primarily focus on natural images, with relatively few studies targeting orthoimages. Unlike natural images, orthoimages have significant geometric attributes and no longer satisfy the epipolar constraint. Therefore, most existing image warping methods are ineffective in dealing with geometric misalignment in orthoimage mosaicking. To solve the above problems, we propose a coarse-to-fine image warping approach using trust region optimization for orthoimage mosaicking. Firstly, we obtain an optimal seamline and search for geometrically misaligned regions along the seamline. Secondly, for each region, we model the geometric alignment problem as an optimization problem of the deformation vectors for pixels. Next, the trust region method is employed to iteratively solve the optimization problem. We introduced the image pyramid strategy to achieve efficient coarse-to-fine optimization and help to avoid local optima. Finally, the orthoimages are warped based on the optimized pixel deformation vectors. However, when dealing with misaligned regions covered by multiple images, the frame-to-frame warping cannot achieve the optimal result. Therefore, we propose a multi-image joint optimization strategy that introduces auxiliary variables to fuse information from multiple orthoimages, ensuring seamless mosaicking. Experimental results demonstrate that whether in two-orthoimage or multi-orthoimage mosaicking, our solution has better visual and quantitative performance.