National Repository of Grey Literature 72 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Calibration parameters of the UAV laser scanning
Dvořák, Dennis ; Potůčková, Markéta (advisor) ; Dušánek, Petr (referee)
The spatial accuracy of points obtained from aerial laser scanning is most affected by the parameters of the GNSS receiver used, the IMU unit and the parameters of the flight itself. An important role is played by the accuracy of the so-called calibration, ie the determination of the transformation elements between the coordinate systems of the scanning unit itself, the IMU and the position of the phase center of the GNSS antenna. The diploma thesis deals with the calibration accuracy of the IMU / GNSS unit. It compares the displacements and rotations of a point cloud acquired by LiDAR (laser scanner RIEGL miniVUX-1UAV) in relation to the calibration elements given by the manufacturer. Evaluate the results using calculated standard deviations and positional differences in the raw point cloud, or by comparing point clouds obtained by another method. It also focuses on verifying geometric accuracy using checkpoints. The accuracy of the IMU / GNSS calibration is minimal after verifying the results and comparing the influence of the calibration elements given by the manufacturer. There was no significant improvement in the quality of the point cloud. At the same time, it was found that for better quality of the scanned data, it is necessary to perform cross-flights when UAV scanning. Key words UAV,...
Deep learning for tree line ecotone mapping from remote sensing data
Dvořák, Jakub ; Potůčková, Markéta (advisor) ; Lefèvre, Sébastien (referee)
Deep learning is growing in popularity in the remote sensing community, especially as a classification algorithm. First part of this thesis describes deep neural networks commonly used for remote sensing classification and their various applications. Capabilities of selected geospatial software suites in relation to deep models are also discussed in this part. Theoretical findings from the first part of the thesis are validated using two deep convolutional Encoder-Decoder networks - U-Net and its proposed adaptation called KrakonosNet. They are used to perform a sematic segmentation of spruce trees and dwarf pine shrubs in the tree line ecotone of the Krkonoše Mountains, Czechia. A normalised digital surface model is employed for creation of sufficiently large amount of training data, while the classification itself is performed using only optical imagery with very high spatial resolution. Resulting classification is compared to a set of traditional remote sensing classifiers, namely Maximum Likelihood, Random Forest, and a Support Vector Machine. Both U-Net and KrakonosNet significantly outperform the other classifiers on this dataset and will be consequently used in a related research project. Key words deep learning, U-Net, Krkonoše mountains, classification, vegetation mapping, picea abies,...
Blocky accumulation mapping from RPAS LiDAR and image data
Kolář, Michal ; Potůčková, Markéta (advisor) ; Dušánek, Petr (referee)
With merit of constant development in measuring technology it is possible to obtain data of high resolution and accuracy describing Earth's surface. During the project "Vegetation and Krkonoše tundra change detection method development by analyzing data from multispectral, hyperspectral and LiDAR UAV sensors" high quality data were acquired, with point density reaching up to 800 points/m2 and orthophoto of GSD 0.02 m. Data are capturing cryoplanation terraces in NE parth of Luční hora in three time periods: June, July and August 2019. The aim of this work is to devise a methodology of blocky accumulation mapping and evaluating detail of data. Key words: blocky accumulation, laser scanning, UAV, point cloud, orthophoto, segmentation
Building modeling from airborne laser scanning point clouds of low density
Hofman, Petr ; Potůčková, Markéta (advisor) ; Šíma, Jiří (referee)
A Laser scanning is a relatively recent remote sensing method which nevertheless quickly gained a prominent position, especially in the area of building detection and 3D modeling. Methods for building detection and 3D modeling initially used model-driven approaches which compare a laser scanning point cloud to a set of predefined building models. A method for determining building roof types using such approaches was presented in the article of Hofman, Potůčková (2012). An important advantage of model-driven approaches is their relative robustness to various data deficiencies such as low point density or low spatial accuracy. However, output of such methods is limited to a predefined set of building models and does not allow for diversity of actual buildings. For this reason, approaches used almost exclusively nowadays are data-driven. These methods search in datasets for a set of primitives (mostly roof planes) that are subsequently used to form the final model. This approach benefits from universality of resulting models but requires generally high data quality, especially in respect to input point cloud densities. The study of Hofman, Potůčková (2017) presented a data-driven method that can reliably detect buildings even in a very sparse point cloud in spite of using data-driven approach. At a density of...
Detection of overhangs from airborne laserscanning data
Ondrušková, Kateřina ; Lysák, Jakub (advisor) ; Potůčková, Markéta (referee)
Detection of overhangs from airborne laserscanning data Abstract The objective of this thesis is to propose a method of processing airborne laserscanning data that include areas with overhangs with the goal of creating 2,5D digital terrain models. The theoretical part consists of brief introduction of basic principles of airborne laserscanning, followed by research of existing published solutions dealing with the problem of overhangs in airborne laserscanning data. Last section of theoretical part of the thesis deals with the usage of airborne laserscanning data in Czechia and digital terrain models created from those data are introduced. In practical part of the thesis, own method of processing of ALS data in the areas of overhangs is presented. The designed algorithm is then implemented in a form of script for ArcGIS 10 and the results are tested using real data from Czech Switzerland. The conclusion focuses on critical evaluation of the suggested method and its implementation. Keywords: overhangs, airborne laserscanning, digital terrain model
Training and validation dataset optimization for Earth observation classification accuracy improvement
Potočná, Barbora ; Kupková, Lucie (advisor) ; Potůčková, Markéta (referee)
This thesis deals with training dataset and validation dataset for Earth observation classification accuracy improvement. Experiments with training data and validation data for two classification algorithms (Maximum Likelihood - MLC and Support Vector Machine - SVM) are carried out from the forest-meadow landscape located in the foothill of the Giant Mountains (Podkrkonoší). The thesis is base on the assumption that 1/3 of training data and 2/3 of validation data is an ideal ratio to achieve maximal classification accuracy (Foody, 2009). Another hypothesis was that in a case of SVM classification, a lower number of training point is required to achieve the same or similar accuracy of classification, as in the case of the MLC algorithm (Foody, 2004). The main goal of the thesis was to test the influence of proportion / amount of training and validation data on the classification accuracy of Sentinel - 2A multispectral data using the MLC algorithm. The highest overal accuracy using the MLC classification algorithm was achieved for 375 training and 625 validation points. The overal accuracy for this ratio was 72,88 %. The theory of Foody (2009) that 1/3 of training data and 2/3 of validation data is an ideal ratio to achieve the highest classification accuracy, was confirmed by the overal accuracy and...
Classification of selected agricultural crops from time series of Sentinel-2 and PlanetScope imagery in Kutnohorsko model area
Kuthan, Tomáš ; Kupková, Lucie (advisor) ; Potůčková, Markéta (referee)
Classification of selected agricultural crops from time series of Sentinel-2 and PlanetScope imagery in Kutnohorsko model area Abstract The thesis is focused on the analysis of spectral characteristics of selected agricultural crops druring agriculutural season from time series of Sentinel -2 (A and B) and PlanetScope sensors in the model area situated around the settlements of Kolín and Kutná Hora. It is based on the assumption that the use of multiple dates of image data acquired crops in different phenological phases of the crops allows better identification of crop species (Lu et al., 2004). The aim of the thesis was to analyse the characteristics of the seasonal course of spectral features of selected agricultural crops (sugar beet, spring barley, winter barley, maize, spring wheat, winter wheat, winter rape) and to determine the period of the year suitable for the differentiation of individual crops. Another aim of the thesis was to classify these crops in the model area from time series of two above-mentioned sensors and to compare the accuracy of the pixel and object-oriented classification approach for multitemporal composites and the accuracy for monotemporal image from the term when the individual crops are clearly distinguishable. The training and validation datasets and the classification mask...
Filtration of Airborne Laserscanning Data
Jančovič, Marián ; Lysák, Jakub (advisor) ; Potůčková, Markéta (referee)
Filtration of Airborne Laserscanning Data Abstract The diploma thesis deals with automatized area classification with different surfaces in rocky towns. These areas are input into Airborne Laser-Scanning data filtration, where each of the areas is filtered with different parameters. The reason for this approach is the extreme height variety and dense vegetation typical for rocky towns, which causes the inadequacy of most common filtration algorithms applied with the same set of parameters for whole area. The methodic proposed by us consists of splitting the area of interest into three parts: Residential area, rocky area and area of vegetation (i.e. area that doesn't contain rocks or buildings). Each of these has special parameters applied during the following filtration, which reflect its characteristics. Key words: laser scanning, DTM, rocky towns, area based filtration
Grassland management monitoring based on Sentinel-1 data
Doležal, Jan ; Potůčková, Markéta (advisor) ; Brodský, Lukáš (referee)
The main goal of this diploma thesis was to find and quantify the connection between coherence, entropy, polarimetric angle alpha obtained from Sentinel-1 radar data and grass cutting/pastures. The research was carried out in the area of the Krkonoše national park. To assemble and validate applied methodology, field data was collected 5 times. Hourly rainfall data from Czech hydrometeorological institute was available, but it did not have to be used - no rainfalls were recorded at the time of data acquisitions. Dependence between mowing and the value of coherence has been confirmed. After mowing, median coherence was higher than before mowing. The results were similar to VH as well as VV polarization. Coherence on polygons remained higher after 12-24 days. In total, two different data acquisition geometries (ascending and descending) were examined. The results in both cases were similar. For polarimetric parameters, no correlation between polarimetric parameters and grass mowing or pasture has been confirmed. Keywords: radar, SAR, Sentinel-1, coherence, polarimetry, grass mowing

National Repository of Grey Literature : 72 records found   1 - 10nextend  jump to record:
See also: similar author names
1 POTŮČKOVÁ, Magdaléna
3 Potůčková, Marie
2 Potůčková, Martina
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