scholarly journals A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

2020 ◽  
Vol 12 (14) ◽  
pp. 2283
Author(s):  
Rushikesh Battulwar ◽  
Garrett Winkelmaier ◽  
Jorge Valencia ◽  
Masoud Zare Naghadehi ◽  
Bijan Peik ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
High Resolution ◽  
Power Consumption ◽  
Fitness Function ◽  
Empirical Studies ◽  
3D Models ◽  
Open Pit ◽  
High Resolution Imagery ◽  
High Resolution Images ◽  
Using Data

High-resolution terrain models of open-pit mine highwalls and benches are essential in developing new automated slope monitoring systems for operational optimization. This paper presents several contributions to the field of remote sensing in surface mines providing a practical framework for generating high-resolution images using low-trim Unmanned Aerial Vehicles (UAVs). First, a novel mobile application was developed for autonomous drone flights to follow mine terrain and capture high-resolution images of the mine surface. In this article, case study is presented showcasing the ability of developed software to import area terrain, plan the flight accordingly, and finally execute the area mapping mission autonomously. Next, to model the drone’s battery performance, empirical studies were conducted considering various flight scenarios. A multivariate linear regression model for drone power consumption was derived from experimental data. The model has also been validated using data from a test flight. Finally, a genetic algorithm for solving the problem of flight planning and optimization has been employed. The developed power consumption model was used as the fitness function in the genetic algorithm. The designed algorithm was then validated using simulation studies. It is shown that the offered path optimization can reduce the time and energy of high-resolution imagery missions by over 50%. The current work provides a practical framework for stability monitoring of open-pit highwalls while achieving required energy optimization and imagery performance.

scholarly journals Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces

2020 ◽  
Vol 12 (7) ◽  
pp. 1144
Author(s):  
Rosa Aguilar ◽  
Monika Kuffer
Keyword(s):  
High Resolution ◽  
Open Space ◽  
Local Planning ◽  
Open Spaces ◽  
High Resolution Imagery ◽  
Coarse Spatial Resolution ◽  
Multi Temporal ◽  
High Resolution Images ◽  
Cloud Computation

Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is promoted by SDG indicator 11.7.1; however, data on this indicator are not readily available, neither globally nor at the metropolitan scale in support of local planning, health and environmental policies. Existing global datasets on built-up areas omit many open spaces due to the coarse spatial resolution of input imagery. Our study presents a novel cloud computation-based method to map open spaces by accessing the multi-temporal high-resolution imagery repository of Planet. We illustrate the benefits of our proposed method for mapping the dynamics and spatial patterns of open spaces for the city of Kampala, Uganda, achieving a classification accuracy of up to 88% for classes used by the Global Human Settlement Layer (GHSL). Results show that open spaces in the Kampala metropolitan area are continuously decreasing, resulting in a loss of open space per capita of approximately 125 m2 within eight years.

scholarly journals SAR Image Segmentation Using Morphological Attribute Profiles

2014 ◽  
Vol XL-3 ◽  
pp. 39-44 ◽  
Author(s):  
M. Boldt ◽  
A. Thiele ◽  
K. Schulz ◽  
S. Hinz
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Region Growing ◽  
Object Based Image Analysis ◽  
Sar Image Segmentation ◽  
High Resolution Imagery ◽  
Object Based ◽  
High Resolution Images ◽  
Novel Method ◽  
Image Set

In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. <br><br> In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.

Photogrammetric processing and application of multi-temporal satellite imagery stereopairs

2021 ◽  
Vol 974 (8) ◽  
pp. 36-44
Author(s):  
R.V. Permyakov
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Weather Conditions ◽  
3D Models ◽  
High Resolution Imagery ◽  
Multi Temporal ◽  
Unfavourable Weather ◽  
High Resolution Satellite Imagery ◽  
Very High Resolution Imagery ◽  
Very High

Stereopairs of very-high resolution satellite imagery constitute one of the key high-accurate data sources on heights. A stereophotogrammetric technique is a key method of processing these data. Despite that a number of spacecrafts gathering very-high-resolution imagery in a stereo mode constantly increases, the area of the Earth regularly covered by such data and stored in the archives of RSD operators remains relatively small and, as a rule, is limited only to large urban agglomerations. The new collection may not suit the customer for several reasons. Firstly, the materials of the new stereo collection are more expensive than those of the archived one. Secondly, due to unfavourable weather conditions and a busy schedule of satellites, the completion of the new collection may go beyond the deadline specified by the customer. Well known and brand-new criteria to form multi-temporal, stereopairs are analyzed. The specific of photogrammetric processing multi-temporal stereopairs is demonstrated. Application of multi-temporal stereopairs is described. In conclusion it is confirmed that 3D-models and high accurate DTMs can be generated basing on stereo models from multi-temporal satellite imagery in the absence of the following data

Autonomous Ortho-Rectification of Very High Resolution Imagery Using SIFT and Genetic Algorithm

2016 ◽  
Vol 82 (5) ◽  
pp. 377-388 ◽  
Author(s):  
Pramod Kumar Konugurthi ◽  
Raghavendra Kune ◽  
Ravi Nooka ◽  
Venkatraman Sarma
Keyword(s):  
Genetic Algorithm ◽  
High Resolution ◽  
High Resolution Imagery ◽  
Very High Resolution Imagery ◽  
Very High

scholarly journals An Improved Method for Impervious Surface Mapping Incorporating LiDAR Data and High-Resolution Imagery at Different Acquisition Times

2018 ◽  
Vol 10 (9) ◽  
pp. 1349 ◽  
Author(s):  
Hui Luo ◽  
Le Wang ◽  
Chen Wu ◽  
Lei Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Sensor Data ◽  
Lidar Data ◽  
Landscape Changes ◽  
Surface Mapping ◽  
High Resolution Imagery ◽  
High Resolution Images

Impervious surface mapping incorporating high-resolution remote sensing imagery has continued to attract increasing interest, as it can provide detailed information about urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping yields better performance than the use of high-resolution imagery alone. However, due to LiDAR data’s high cost of acquisition, it is difficult to obtain LiDAR data that was acquired at the same time as the high-resolution imagery in order to conduct impervious surface mapping by multi-sensor remote sensing data. Consequently, the occurrence of real landscape changes between multi-sensor remote sensing data sets with different acquisition times results in misclassification errors in impervious surface mapping. This issue has generally been neglected in previous works. Furthermore, observation differences that were generated from multi-sensor data—including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images—also present obstacles to achieving the final mapping result in the fusion of LiDAR data and high-resolution images. In order to resolve these issues, we propose an improved impervious surface-mapping method incorporating both LiDAR data and high-resolution imagery with different acquisition times that consider real landscape changes and observation differences. In the proposed method, multi-sensor change detection by supervised multivariate alteration detection (MAD) is employed to identify the changed areas and mis-registered areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution image are extracted via independent classification based on the corresponding single-sensor data. Finally, an object-based post-classification fusion is proposed that takes advantage of both independent classification results while using single-sensor data and the joint classification result using stacked multi-sensor data. The impervious surface map is subsequently obtained by combining the landscape classes in the accurate classification map. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and unambiguously improve the performance of impervious surface mapping.

scholarly journals Effect of photogrammetric RPAS flight parameters on plani-altimetric accuracy of DTM

2020 ◽  
Vol 12 (1) ◽  
pp. 1017-1035
Author(s):  
Zuriel Dathan Mora-Felix ◽  
Antonio Jesus Sanhouse-Garcia ◽  
Yaneth A. Bustos-Terrones ◽  
Juan G. Loaiza ◽  
Sergio Alberto Monjardin-Armenta ◽  
...  
Keyword(s):  
High Resolution ◽  
Orthogonal Design ◽  
Low Cost ◽  
Image Data ◽  
Point Clouds ◽  
3D Models ◽  
Digital Terrain ◽  
High Resolution Imagery ◽  
Flight Parameters ◽  
Aerial Systems

AbstractRemotely piloted aerial systems (RPASs) are gaining fast and wide application around the world due to its relative low-cost advantage in the acquisition of high-resolution imagery. However, standardized protocols for the construction of cartographic products are needed. The aim of this paper is to optimize the generation of digital terrain models (DTMs) by using different RPAS flight parameters. An orthogonal design L18 was used to measure the effect of photogrammetric flight parameters on the DTM generated. The image data were acquired using a DJI Phantom 4 Pro drone and six flight parameters were evaluated: flight mode, altitude, flight speed, camera tilt, longitudinal overlap and transversal overlap. Fifty-one ground control points were established using a global positioning system. Multivision algorithms were used to obtain ultra-high resolution point clouds, orthophotos and 3D models from the photos acquired. Root mean square error was used to measure the geometric accuracy of DTMs generated. The effect of photogrammetric flight parameters was carried out by using analysis of variance statistical analysis. Altimetric and planimetric accuracies of 0.38 and 0.11 m were achieved, respectively. Based on these results, high-precision cartographic material was generated using low-cost technology.

scholarly journals A Tidal Flat Wetlands Delineation and Classification Method for High-Resolution Imagery

2021 ◽  
Vol 10 (7) ◽  
pp. 451
Author(s):  
Hong Pan ◽  
Yonghong Jia ◽  
Dawei Zhao ◽  
Tianyu Xiu ◽  
Fuzhi Duan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Tidal Flat ◽  
Classification Method ◽  
Wetland Delineation ◽  
Prediction Algorithm ◽  
High Resolution Imagery ◽  
High Resolution Images ◽  
Shoreline Prediction ◽  
Tidal Correction

As an important part of coastal wetlands, tidal flat wetlands provide various significant ecological functions. Due to offshore pollution and unreasonable utilization, tidal flats have been increasingly threatened and degraded. Therefore, it is necessary to protect and restore this important wetland by monitoring its distribution. Considering the multiple sizes of research objects, remote sensing images with high resolutions have unique resolution advantages to support the extraction of tidal flat wetlands for subsequent monitoring. The purpose of this study is to propose and evaluate a tidal flat wetland delineation and classification method from high-resolution images. First, remote sensing features and geographical buffers are used to establish a decision tree for initial classification. Next, a natural shoreline prediction algorithm is designed to refine the range of the tidal flat wetland. Then, a range and standard deviation descriptor is constructed to extract the rock marine shore, a category of tidal flat wetlands. A geographical analysis method is considered to distinguish the other two categories of tidal flat wetlands. Finally, a tidal correction strategy is introduced to regulate the borderline of tidal flat wetlands to conform to the actual situation. The performance of each step was evaluated, and the results of the proposed method were compared with existing available methods. The results show that the overall accuracy of the proposed method mostly exceeded 92% (all higher than 88%). Due to the integration and the performance superiority compared to existing available methods, the proposed method is applicable in practice and has already been applied during the construction project of Hengqin Island in China.

The 3D Virtual Campus Modeling Based on High Resolution Images from Remote Sensors

2012 ◽  
Vol 170-173 ◽  
pp. 2844-2847
Author(s):  
Zhen Zhi Wu ◽  
Hong Yan Wen ◽  
Gui Wen Lan
Keyword(s):  
Information System ◽  
High Resolution ◽  
3D Visualization ◽  
3D Models ◽  
Management Information ◽  
Remote Sensors ◽  
University Of Technology ◽  
Virtual Reconstruction ◽  
Remotely Sensed Image ◽  
High Resolution Images

With the high resolution remotely sensed image and an old map of the Pingfeng campus of Guilin University of Technology, 3D virtual reconstruction, roaming and buildings management of the campus were accomplished by the usage of ArcGIS to get basic geographic information, applying SketchUp to create 3D models and ArcEngine to develop a management information system. This method takes full advantage of existing data sources and can quickly build a 3D visualization information system.

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