scholarly journals An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2118
Author(s):  
Pyung-chae Lim ◽  
Sooahm Rhee ◽  
Junghoon Seo ◽  
Jae-In Kim ◽  
Junhwa Chi ◽  
...  
Keyword(s):  
Large Scale ◽  
Region Of Interest ◽  
Selection Algorithm ◽  
Initial Image ◽  
Minimum Number ◽  
Image Pairs ◽  
Uav Images ◽  
Uav Image ◽  
Orientation Parameters ◽  
Easy Operation

Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters as well as analyzing the sharpness of they themselves measured through image analysis. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW) and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.

scholarly journals An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

2021 ◽  
Author(s):  
Pyung-chae Lim ◽  
Junghoon Seo ◽  
Jae-in Kim ◽  
Sooahm Rhee ◽  
Junhwa Chi ◽  
...  
Keyword(s):  
Large Scale ◽  
Region Of Interest ◽  
Selection Algorithm ◽  
Initial Image ◽  
Minimum Number ◽  
Image Pairs ◽  
Uav Images ◽  
Uav Image ◽  
Orientation Parameters ◽  
Easy Operation

Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips, and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW), and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.

scholarly journals ANALYSIS OF ORIENTATION ACCURACY OF AN UAV IMAGE ACCORDING TO CAMERA CALIBRATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 437-442 ◽  
Author(s):  
P. C. Lim ◽  
J. Seo ◽  
J. Son ◽  
T. Kim
Keyword(s):  
Camera Calibration ◽  
Exterior Orientation ◽  
Time Saving ◽  
Commercial Grade ◽  
Sensing Platforms ◽  
Small Uav ◽  
Uav Image ◽  
Orientation Parameters ◽  
Easy Operation

<p><strong>Abstract.</strong> Utilization of an UAV is increasing because of its easy operation and time saving advantages. Compared with other remote sensing platforms, the biggest difference of a small UAV is the unstable flight attitude due to platform stability. UAVs are equipped with a commercial grade camera, unlike expensive cameras mounted on manned aircraft or satellite platforms. The quality of the map is determined by the characteristics of an UAV and camera performance. In this study, the accuracy of orientation parameters according to UAV camera calibration options was analysed. The camera calibration options were no calibration, self-calibration and calibration by a public calibration toolkit with manual corner measurement. We used four different type of UAVs and three type of SWs. Interior and exterior orientation parameters according to the camera calibration options were obtained from each software. The result of processing by each camera calibration option was different from each other. This may indicate that the UAV camera calibration was not performed accurately and still needed further improvement.</p>

scholarly journals Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 993
Author(s):  
Yingying Kong ◽  
Bowen Zhang ◽  
Biyuan Yan ◽  
Yanjuan Liu ◽  
Henry Leung ◽  
...  
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Region Of Interest ◽  
Semantic Segmentation ◽  
Geographical Information ◽  
Aerial Image ◽  
Surface Model ◽  
Uav Images ◽  
Uav Image ◽  
Multiple Metrics

Unmanned aerial vehicles (UAV) have had significant progress in the last decade, which is applied to many relevant fields because of the progress of aerial image processing and the convenience to explore areas that men cannot reach. Still, as the basis of further applications such as object tracking and terrain classification, semantic image segmentation is one of the most difficult challenges in the field of computer vision. In this paper, we propose a method for urban UAV images semantic segmentation, which utilizes the geographical information of the region of interest in the form of a digital surface model (DSM). We introduce an Affiliated Fusion Conditional Random Field (AF-CRF), which combines the information of visual pictures and DSM, and a multi-scale strategy with attention to improve the segmenting results. The experiments show that the proposed structure performs better than state-of-the-art networks in multiple metrics.

scholarly journals Using Information Content to Select Keypoints for UAV Image Matching

2021 ◽  
Vol 13 (7) ◽  
pp. 1302
Author(s):  
Vahid Mousavi ◽  
Masood Varshosaz ◽  
Fabio Remondino
Keyword(s):  
Information Content ◽  
Image Matching ◽  
Texture Coefficient ◽  
Screening Method ◽  
Bundle Adjustment ◽  
Selection Methods ◽  
Selection Tasks ◽  
Image Pairs ◽  
Uav Images ◽  
Uav Image

Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

scholarly journals A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3921 ◽  
Author(s):  
Wuttichai Boonpook ◽  
Yumin Tan ◽  
Yinghua Ye ◽  
Peerapong Torteeka ◽  
Kritanai Torsri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Water Levels ◽  
Building Detection ◽  
Network Training ◽  
Building Information ◽  
Open Standard ◽  
Uav Images ◽  
Uav Image

Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.

scholarly journals A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION

2016 ◽  
Vol III-7 ◽  
pp. 111-117
Author(s):  
Ping Wang ◽  
Zheng Wei ◽  
Weihong Cui ◽  
Zhiyong Lin
Keyword(s):  
Image Segmentation ◽  
Coastal Area ◽  
Minimum Spanning Tree ◽  
Edge Weight ◽  
Segmentation Method ◽  
Optimal Criterion ◽  
The Rich ◽  
Uav Images ◽  
Uav Image ◽  
Scale Control

This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

scholarly journals DENSE 3D POINT CLOUD GENERATION FROM UAV IMAGES FROM IMAGE MATCHING AND GLOBAL OPTIMAZATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 1005-1009 ◽  
Author(s):  
S. Rhee ◽  
T. Kim
Keyword(s):  
Global Optimization ◽  
Image Matching ◽  
Point Clouds ◽  
Image Space ◽  
Object Space ◽  
3D Point Clouds ◽  
Local Point ◽  
Matching Technique ◽  
Uav Images ◽  
Orientation Parameters

3D spatial information from unmanned aerial vehicles (UAV) images is usually provided in the form of 3D point clouds. For various UAV applications, it is important to generate dense 3D point clouds automatically from over the entire extent of UAV images. In this paper, we aim to apply image matching for generation of local point clouds over a pair or group of images and global optimization to combine local point clouds over the whole region of interest. We tried to apply two types of image matching, an object space-based matching technique and an image space-based matching technique, and to compare the performance of the two techniques. The object space-based matching used here sets a list of candidate height values for a fixed horizontal position in the object space. For each height, its corresponding image point is calculated and similarity is measured by grey-level correlation. The image space-based matching used here is a modified relaxation matching. We devised a global optimization scheme for finding optimal pairs (or groups) to apply image matching, defining local match region in image- or object- space, and merging local point clouds into a global one. For optimal pair selection, tiepoints among images were extracted and stereo coverage network was defined by forming a maximum spanning tree using the tiepoints. From experiments, we confirmed that through image matching and global optimization, 3D point clouds were generated successfully. However, results also revealed some limitations. In case of image-based matching results, we observed some blanks in 3D point clouds. In case of object space-based matching results, we observed more blunders than image-based matching ones and noisy local height variations. We suspect these might be due to inaccurate orientation parameters. The work in this paper is still ongoing. We will further test our approach with more precise orientation parameters.

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