Actual Aerial Images Research Articles

Simulating the Appearance of Aerial Images formed by Aerial Imaging by Retroreflection

Ray tracing, combined with ideal retroreflection and mathematical simulation methods, has been used to design aerial imaging systems based on retroreflection. Although aerial images are blurred and have lower brightness than the light source, existing simulation methods do not focus on the appearance of these characteristics. In this study, we propose a computer graphics (CG)-based simulation using the ray tracing method to generate CG renditions of aerial images with reproduced luminance and blurring. CG models of three optical elements (light source, half-mirror, and retroreflector) were created on the basis of existing optical element models to simulate aerial images obtained through retroreflection in aerial imaging systems. By measuring the image formation positioning, we determined that the rendered aerial images consistently formed at a plane-symmetrical position relative to the axis of the half-mirror model, with a mean absolute error of 0.55mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${0.55\\,\ extrm{mm}}$$\\end{document}. We also compared rendered and actual aerial images in terms of luminance and sharpness characteristics, and found that the mean absolute percentage error of luminance remained within 0.0376\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.0376$$\\end{document}. Furthermore, the directional dependence of blur was effectively reproduced using the retroreflector bidirectional reflectance distribution function developed in this study.

A Cooperative Target Localization Method Based on UAV Aerial Images

A passive localization algorithm based on UAV aerial images and Angle of Arrival (AOA) is proposed to solve the target passive localization problem. In this paper, the images are captured using fixed-focus shooting. A target localization factor is defined to eliminate the effect of focal length and simplify calculations. To synchronize the positions of multiple UAVs, a dynamic navigation coordinate system is defined with the leader at its center. The target positioning factor is calculated based on image information and azimuth elements within the UAV photoelectric reconnaissance device. The covariance equation is used to derive AOA, which is then used to obtain the target coordinate value by solving the joint UAV swarm positional information. The accuracy of the positioning algorithm is verified by actual aerial images. Based on this, an error model is established, the calculation method of the co-localization PDOP is given, and the correctness of the error model is verified through the simulation of the Monte Carlo statistical method. At the end of the article, the trackless Kalman filter algorithm is designed to improve positioning accuracy, and the simulation analysis is performed on the stationary and moving states of the target. The experimental results show that the algorithm can significantly improve the target positioning accuracy and ensure stable tracking of the target.

A discovery about the positional distribution pattern among candidate homologous pixels and its potential application in aerial multi-view image matching

In aerial multi-view photogrammetry, whether there is a special positional distribution pattern among candidate homologous pixels of a matching pixel in the multi-view images? If so, can this positional pattern be used to precisely confirm the real homologous pixels? These problems have not been studied at present. Therefore, the study of the positional distribution pattern among candidate homologous pixels based on the adjustment theory in surveying is investigated in this paper. Firstly, the definition and computing method of pixel’s pseudo object-space coordinates are given, which can transform the problem of multi-view matching for confirming real homologous pixels into the problem of surveying adjustment for computing the pseudo object-space coordinates of the matching pixel. Secondly, according to the surveying adjustment theory, the standardized residual of each candidate homologous pixel of the matching pixel is figured out, and the positional distribution pattern among these candidate pixels is theoretically inferred utilizing the quantitative index of standardized residual. Lastly, actual aerial images acquired by different sensors are used to carry out experimental verification of the theoretical inference. Experimental results prove not only that there is a specific positional distribution pattern among candidate homologous pixels, but also that this positional distribution pattern can be used to develop a new object-side multi-view image matching method. The proposed study has an important reference value on resolving the defects of existing image-side multi-view matching methods at the mechanism level.

Aerial image matching method based on HSI hash learning

Specific to characteristics of aerial images like large size, high resolution and rich edges and texture, HSI hash learning matching algorithm for aerial image is proposed. Firstly, the original RGB component image is converted to HSI component image, then HSI-based local multi-feature descriptor is constructed. Secondly, in order to decrease the encoding mapping error in the traditional hash learning during model parameter optimization, objective function for each component image is constructed by the hash degree and the similarity information. And then, the projection matrix and bias threshold are determined. Finally, in the Hamming matching stage, the minimized loss distance function is defined and the spatial similarity between the Euclidean space and the Hamming space is preserved. Thus, the quantization loss in hash learning is reduced significantly. The proposed HSI hash learning algorithm evaluated on the standard dataset and actual aerial images demonstrates that it may significantly outperform the traditional algorithms in terms of efficiency and performance. As shown by experimental results, the matching accuracy of the proposed method is at least 20% higher than that of other binary descriptors, with desired computation time consumption.

An enhanced multi-view vertical line locus matching algorithm of object space ground primitives based on positioning consistency for aerial and space images

The traditional multi-view vertical line locus (TMVLL) matching method is an object-space-based method that is commonly used to directly acquire spatial 3D coordinates of ground objects in photogrammetry. However, the TMVLL method can only obtain one elevation and lacks an accurate means of validating the matching results. In this paper, we propose an enhanced multi-view vertical line locus (EMVLL) matching algorithm based on positioning consistency for aerial or space images. The algorithm involves three components: confirming candidate pixels of the ground primitive in the base image, multi-view image matching based on the object space constraints for all candidate pixels, and validating the consistency of the object space coordinates with the multi-view matching result. The proposed algorithm was tested using actual aerial images and space images. Experimental results show that the EMVLL method successfully solves the problems associated with the TMVLL method, and has greater reliability, accuracy and computing efficiency.

Local Deep Hashing Matching of Aerial Images Based on Relative Distance and Absolute Distance Constraints

Aerial images have features of high resolution, complex background, and usually require large amounts of calculation, however, most algorithms used in matching of aerial images adopt the shallow hand-crafted features expressed as floating-point descriptors (e.g., SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features)), which may suffer from poor matching speed and are not well represented in the literature. Here, we propose a novel Local Deep Hashing Matching (LDHM) method for matching of aerial images with large size and with lower complexity or fast matching speed. The basic idea of the proposed algorithm is to utilize the deep network model in the local area of the aerial images, and study the local features, as well as the hash function of the images. Firstly, according to the course overlap rate of aerial images, the algorithm extracts the local areas for matching to avoid the processing of redundant information. Secondly, a triplet network structure is proposed to mine the deep features of the patches of the local image, and the learned features are imported to the hash layer, thus obtaining the representation of a binary hash code. Thirdly, the constraints of the positive samples to the absolute distance are added on the basis of the triplet loss, a new objective function is constructed to optimize the parameters of the network and enhance the discriminating capabilities of image patch features. Finally, the obtained deep hash code of each image patch is used for the similarity comparison of the image patches in the Hamming space to complete the matching of aerial images. The proposed LDHM algorithm evaluates the UltraCam-D dataset and a set of actual aerial images, simulation result demonstrates that it may significantly outperform the state-of-the-art algorithm in terms of the efficiency and performance.

Seamline Determination Based on Semantic Segmentation for Aerial Image Mosaicking

We propose a novel method for seamline determination based on semantic segmentation for aerial image mosaicking. First, we train a convolutional neural network (CNN) for pixel labeling to extract building regions. Using the trained CNN, we create a building probability map from an input aerial image with no pre-processing. We then use Dijkstra’s algorithm to find the optimal seamline as a shortest path on the map. We evaluate the quality of the seamlines produced by our method on actual aerial images. Finally, we show that our seamlines never pass through any buildings and compare the effectiveness with the conventional mean-shift segmentation-based method.

Insulator Species Recognition Based on Difference Features of the Color Image

This paper proposed a new method of feature description for insulator species recognition. A method of calculating the difference was defined. The three component value matrixes of an image in HSI space were converted to difference value matrixes successively. Then the difference value, shape and angle features of each region and each color were described. Experiments showed that the proposed method can be applied to describe the actual aerial images, and achieved the recognition of insulator species.

Adaptive denoising method to improve aberration measurement performance

An adaptive denoising method utilizing weighted least-square (WLSQ) is proposed for an aberration measurement (AM) method based on aerial images (AI). In the aberration measurement process, the WLSQ method is employed to obtain the principal component coefficients which are used for extracting the Zernike coefficients. In this paper, we also provide a noise model according to the actual aerial images. Based on this noise model, a simplified weighting factor for the WLSQ method is also proposed. Because of the adaptive and lossless denoising ability of the proposed method, more accurate Zernike coefficients can be extracted from aerial images. Compared with the aberration measurement techniques based on principal component analysis of aerial images (AMAI-PCA), the proposed method can enhance the accuracy by more than 30% when the range of Zernike aberrations is within 0.1λ. The experiment also shows that the proposed method can detect the aberration shift more accurately.

The application of GPS precise point positioning technology in aerial triangulation

In traditional GPS-supported aerotriangulation, differential GPS (DGPS) positioning technology is used to determine the 3-dimensional coordinates of the perspective centers at exposure time with an accuracy of centimeter to decimeter level. This method can significantly reduce the number of ground control points (GCPs). However, the establishment of GPS reference stations for DGPS positioning is not only labor-intensive and costly, but also increases the implementation difficulty of aerial photography. This paper proposes aerial triangulation supported with GPS precise point positioning (PPP) as a way to avoid the use of the GPS reference stations and simplify the work of aerial photography. Firstly, we present the algorithm for GPS PPP in aerial triangulation applications. Secondly, the error law of the coordinate of perspective centers determined using GPS PPP is analyzed. Thirdly, based on GPS PPP and aerial triangulation software self-developed by the authors, four sets of actual aerial images taken from surveying and mapping projects, different in both terrain and photographic scale, are given as experimental models. The four sets of actual data were taken over a flat region at a scale of 1:2500, a mountainous region at a scale of 1:3000, a high mountainous region at a scale of 1:32000 and an upland region at a scale of 1:60000 respectively. In these experiments, the GPS PPP results were compared with results obtained through DGPS positioning and traditional bundle block adjustment. In this way, the empirical positioning accuracy of GPS PPP in aerial triangulation can be estimated. Finally, the results of bundle block adjustment with airborne GPS controls from GPS PPP are analyzed in detail. The empirical results show that GPS PPP applied in aerial triangulation has a systematic error of half-meter level and a stochastic error within a few decimeters. However, if a suitable adjustment solution is adopted, the systematic error can be eliminated in GPS-supported bundle block adjustment. When four full GCPs are emplaced in the corners of the adjustment block, then the systematic error is compensated using a set of independent unknown parameters for each strip, the final result of the bundle block adjustment with airborne GPS controls from PPP is the same as that of bundle block adjustment with airborne GPS controls from DGPS. Although the accuracy of the former is a little lower than that of traditional bundle block adjustment with dense GCPs, it can still satisfy the accuracy requirement of photogrammetric point determination for topographic mapping at many scales.