Because the traditional method of image stitching based on feature point detection tends to fracture in the stitching results, As Projective-As Possible (APAP) stitching algorithm proposed by Juilo Zaragoza was improved in this paper to eliminate fracture in stitching overlap area. First of all, the matching feature points were calculated through A-KAZE feature point detection algorithm. Then, the projection model was determined on the basis of Direct Linear Transformation (DLT) algorithm, and the target images were divided into 25×25 blocks, which were projected with APAP algorithm. Besides, multiple homography matrix was calculated. Finally, the global projection transformation model was estimated according to each multiple homography matrix. Considering the traditional image fusion approach makes stitching results discontinuous, and cannot guarantee the integrity of the objects in image overlap, the Min-Cut/Max-Flow of edge detection operator and Laplacian multiresolution fusion algorithm were added in this paper so as to achieve image fusion and realize the seamless stitching of multiple images. The simulation experiments show that the stitching method can eliminate the fracture in the stitching results, and realize the seamless stitching of multiple images.

With the development of remote sensing technology, true-color visualization of hyperspectral LiDAR echo signals has become a hotspot for both academic research and commercial applications. The limitation of the emission power of hyperspectral LiDAR causes the loss of spectral-reflectance information in some channels of the hyperspectral LiDAR echo signal. The color reconstructed based on the hyperspectral LiDAR echo signal is bound to have serious color cast problem. To solve the existing problem, a spectral missing color correction approach based on adaptive parameter fitting model is proposed in this study. Given the known missing spectral-reflectance band intervals, the colors in incomplete spectral integration are corrected to accurately restore target colors. Based on the experimental results, the color difference between color blocks and the hyperspectral image corrected by the proposed color correction model is smaller than that of the ground truth, and the image quality is higher, realizing the accurate reproduction of the target color.

With the development of remote sensing technology, the simultaneous acquisition of 3D point cloud and color information has become the constant goal for scientific research and commercial applications in this field. However, since radar echo data in practice refer to the value of the spectral channel and its corresponding energy, it is still impossible to obtain accurate tristimulus values of the point through color integral calculation after traditional normalization and multispectral correction. Furthermore, the reflectance of the target, the laser transmission power and other factors lead to the problems of no echo energy or weak echo energy in some bands of the visible spectrum, which further leads to large chromatic difference compared to the color calculated from the spectral reflectance of standard color card. In response to these problems, the hyperbolic tangent spectrum correction model with parameters is proposed for the spectrum correction of the acquired hyperspectral LiDAR in the 470–700 nm band. In addition, the improved gradient boosting decision tree sequence prediction algorithm is proposed for the reconstruction of missing spectrum in the 400–470 nm band where the echo energy is weak and missing. Experimental results show that there is relatively small chromatic difference between the obtained spectral information after correction and reconstruction and the spectrum of standard color card, achieving the purpose of true color reconstruction.