A Novel Method for the Deblurring of Photogrammetric Images Using Conditional Generative Adversarial Networks

Blind Deblurring of Remote-Sensing Single Images Based on Feature Alignment

Motion blur recovery is a common method in the field of remote sensing image processing that can effectively improve the accuracy of detection and recognition. Among the existing motion blur recovery methods, the algorithms based on deep learning do not rely on a priori knowledge and, thus, have better generalizability. However, the existing deep learning algorithms usually suffer from feature misalignment, resulting in a high probability of missing details or errors in the recovered images. This paper proposes an end-to-end generative adversarial network (SDD-GAN) for single-image motion deblurring to address this problem and to optimize the recovery of blurred remote sensing images. Firstly, this paper applies a feature alignment module (FAFM) in the generator to learn the offset between feature maps to adjust the position of each sample in the convolution kernel and to align the feature maps according to the context; secondly, a feature importance selection module is introduced in the generator to adaptively filter the feature maps in the spatial and channel domains, preserving reliable details in the feature maps and improving the performance of the algorithm. In addition, this paper constructs a self-constructed remote sensing dataset (RSDATA) based on the mechanism of image blurring caused by the high-speed orbital motion of satellites. Comparative experiments are conducted on self-built remote sensing datasets and public datasets as well as on real remote sensing blurred images taken by an in-orbit satellite (CX-6(02)). The results show that the algorithm in this paper outperforms the comparison algorithm in terms of both quantitative evaluation and visual effects.

Analysing Key Steps of the Photogrammetric Pipeline for Museum Artefacts 3D Digitisation

In recent years, massive digitisation of cultural heritage (CH) assets has become a focus of European programmes and initiatives. Among CH settings, attention is reserved to the immense and precious museum collections, whose digital 3D reproduction can support broader non-invasive analyses and stimulate the realisation of more attractive and interactive exhibitions. The reconstruction pipeline typically includes numerous processing steps when passive techniques are selected to deal with object digitisation. This article presents some insights on critical operations, which, based on our experience, can rule the quality of the final models and the reconstruction times for delivering 3D heritage results, while boosting the sustainability of digital cultural contents. The depth of field (DoF) problem is explored in the acquisition phase when surveying medium and small-sized objects. Techniques for deblurring images and masking object backgrounds are examined relative to the pre-processing stage. Some point cloud denoising and mesh simplification procedures are analysed in data post-processing. Hints on physically-based rendering (PBR) materials are also presented as closing operations of the reconstruction pipeline. This paper explores these processes mainly through experiments, providing a practical guide, tricks, and suggestions when tackling museum digitisation projects.

Vectorized rooftop area data for 90 cities in China

Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km2 across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively.

Histogram Adjustment of Images for Improving Photogrammetric Reconstruction

The accuracy of photogrammetric reconstruction depends largely on the acquisition conditions and on the quality of input photographs. This paper proposes methods of improving raster images that increase photogrammetric reconstruction accuracy. These methods are based on modifying color image histograms. Special emphasis was placed on the selection of channels of the RGB and CIE L*a*b* color models for further improvement of the reconstruction process. A methodology was proposed for assessing the quality of reconstruction based on premade reference models using positional statistics. The analysis of the influence of image enhancement on reconstruction was carried out for various types of objects. The proposed methods can significantly improve the quality of reconstruction. The superiority of methods based on the luminance channel of the L*a*b* model was demonstrated. Our studies indicated high efficiency of the histogram equalization method (HE), although these results were not highly distinctive for all performed tests.

Polymodal Method of Improving the Quality of Photogrammetric Images and Models

Photogrammetry using unmanned aerial vehicles has become very popular and is already commonly used. The most frequent photogrammetry products are an orthoimage, digital terrain model and a 3D object model. When executing measurement flights, it may happen that there are unsuitable lighting conditions, and the flight itself is fast and not very stable. As a result, noise and blur appear on the images, and the images themselves can have too low of a resolution to satisfy the quality requirements for a photogrammetric product. In such cases, the obtained images are useless or will significantly reduce the quality of the end-product of low-level photogrammetry. A new polymodal method of improving measurement image quality has been proposed to avoid such issues. The method discussed in this article removes degrading factors from the images and, as a consequence, improves the geometric and interpretative quality of a photogrammetric product. The author analyzed 17 various image degradation cases, developed 34 models based on degraded and recovered images, and conducted an objective analysis of the quality of the recovered images and models. As evidenced, the result was a significant improvement in the interpretative quality of the images themselves and a better geometry model.

UAV Photogrammetry under Poor Lighting Conditions-Accuracy Considerations

The use of low-level photogrammetry is very broad, and studies in this field are conducted in many aspects. Most research and applications are based on image data acquired during the day, which seems natural and obvious. However, the authors of this paper draw attention to the potential and possible use of UAV photogrammetry during the darker time of the day. The potential of night-time images has not been yet widely recognized, since correct scenery lighting or lack of scenery light sources is an obvious issue. The authors have developed typical day- and night-time photogrammetric models. They have also presented an extensive analysis of the geometry, indicated which process element had the greatest impact on degrading night-time photogrammetric product, as well as which measurable factor directly correlated with image accuracy. The reduction in geometry during night-time tests was greatly impacted by the non-uniform distribution of GCPs within the study area. The calibration of non-metric cameras is sensitive to poor lighting conditions, which leads to the generation of a higher determination error for each intrinsic orientation and distortion parameter. As evidenced, uniformly illuminated photos can be used to construct a model with lower reprojection error, and each tie point exhibits greater precision. Furthermore, they have evaluated whether commercial photogrammetric software enabled reaching acceptable image quality and whether the digital camera type impacted interpretative quality. The research paper is concluded with an extended discussion, conclusions, and recommendation on night-time studies.

Observability Analysis and Navigation Filter Optimization of High-Orbit Satellite Navigation System Based on GNSS

Global Navigation Satellite System (GNSS) can be applied for the navigation of the high-orbit satellites. The system observability will change due to the changes in the visible satellite numbers and the spatial geometry between the navigation satellites and the users in the navigation system. The influence of the observability changing is not considered in the traditional navigation filter algorithm. In this paper, an optimized navigation filter method based on observability analysis is proposed. Firstly, a novel criterion for the relative observable degree is proposed for each observation component by making use of observation data from previous and posterior time simultaneously. Secondly, according to the relationship between observability and navigation filter accuracy, a novel optimized navigation filter method is constructed by introducing an adjusting factor based on the relative observable degree. Through the comparative simulations with the traditional Extended Kalman Filter (EKF), the optimized navigation filter method can reduce the estimation error of position and velocity by about 36% and 44% respectively. Therefore, the superiority of the proposed filter optimization algorithm is verified.