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Extraction of Landscape Elements from Remote Sensing Data
Ferencz, Jakub ; Kalvoda, Petr (referee) ; Hanzl, Vlastimil (advisor)
This master thesis deals with a classification technique for an automatic detection of different land cover types from combination of high resolution imagery and LiDAR data sets. The main aim is to introduce additional post-processing method to commonly accessible quality data sets which can replace traditional mapping techniques for certain type of applications. Classification is the process of dividing the image into land cover categories which helps with continuous and up-to-date monitoring management. Nowadays, with all the technologies and software available, it is possible to replace traditional monitoring methods with more automated processes to generate accurate and cost-effective results. This project uses object-oriented image analysis (OBIA) to classify available data sets into five main land cover classes. The automate classification rule set providing overall accuracy of 88% of correctly classified land cover types was developed and evaluated in this research. Further, the transferability of developed approach was tested upon the same type of data sets within different study area with similar success – overall accuracy was 87%. Also the limitations found during the investigation procedure are discussed and brief further approach in this field is outlined.
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Automatic extraction of buildings and imprevious areas from very high resolution data in suburban area of Prague
Horňáková, Markéta ; Kupková, Lucie (advisor) ; Potůčková, Markéta (referee)
7 Automatic extraction of buildings and imprevious areas from very high resolution data in suburban area of Prague Abstract Nowdays, when the very high resolution satellite imagery and airbone laser scanning data have became more accessible, the possibility of their use for different types of applications increased also. With a rapid development of urban hinterlands the demand to monitor these areas increases also with the goal to avoid uncoordinated construction. This work therefore focuses on an object oriented based classification in order to design its own methodological approach for the extraction of buildings and imprevious areas in selected areas of commercial suburbanization in the Prague hinterland. The aim is among others to find the classification rules for distinguishing different types of roofs and impervious areas depending on the material, shape characteristic etc. The literature overview summarizes methods of buildings and imprevious areas classification and extraction using very high resolution optical data and elevation data. Very high resolution QuickBird imagery and airbone laser scanning LIDAR elevation data and object oriented classification methods were used for and analysis of selected commercial suburbanization model areas in Prague hinterland. The proposed methodology uses...
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Assessments of forest damage using satellite and LIDAR data
Lihanová, Kristýna ; Štych, Přemysl (advisor) ; Bartaloš, Tomáš (referee)
Assessment of forest damage using satellite and lidar data Abstract The main objective of this thesis is to create a methodical procedure used for the evaluation of forest damage in the chosen area of the National Park Sumava, Czech Republic. In this work were combined the multispectral satellite data and data of airborne laser scanning. The forests in this area are heavily damaged mainly due to bark beetle outbreak. You can find here as healthy so damaged forests. Based on this methodology will be differentiated greater number of classes than I found in the literature. In this work was used pansharpened multispectral image SPOT, multispectral image Landsat and airborne laser scanning data with low density points. Another task was to get height information from ALS data in the form of grid. Forest stands were classified using object-oriented classification, which included at first segmentation and then creation of classification base. In classification entered spectral information and height information obtained from the ALS data. Forests were classified into 5 classes and accuracy of both classifications was evaluated using the error matrix and kappa coefficient. SPOT image classification reached kappa coefficient of 68,5 % and Landsat image classification reached kappa coefficient of 72,3 %. From the...
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Recognition and classification of patterned ground polygons from remote sensing data
Kříž, Jan ; Potůčková, Markéta (advisor) ; Brodský, Lukáš (referee)
Recognition and classification of patterned ground polygons from remote sensing data Abstract The main objective of this thesis has been to prove the possibility of using object based image analysis classification for identification of the ice-wedge polygons and to find general method for their classification. The thesis contains a comparison of the object based and pixel based classification of the subject. The three classification rulesets for OBIA were developed on three test sites on Mars captured by HiRISE sensor. As a result, the general classification approach is suggested. The manually collected datasets, which are common in geomorphological research, were used as the reference sample. The OBIA classification provided better results in all three cases, whereas the pixel classification was valid in only one case. Another objective has been the automatization of the process of gaining information about morphometric characteristics of the ice-wedge polygons and the subsequent classification of the polygons. Within the scope of the process were developed methods for creating polygonal network and specified parameters of those methods. Several toolboxes for the ArcGIS software were prepared and they are part of the results of the thesis. Keywords: patterned ground, ice-wedge polygons, remote sensing,...
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Automatic extraction of buildings and imprevious areas from very high resolution data in suburban area of Prague
Horňáková, Markéta ; Kupková, Lucie (advisor) ; Potůčková, Markéta (referee)
7 Automatic extraction of buildings and imprevious areas from very high resolution data in suburban area of Prague Abstract Nowdays, when the very high resolution satellite imagery and airbone laser scanning data have became more accessible, the possibility of their use for different types of applications increased also. With a rapid development of urban hinterlands the demand to monitor these areas increases also with the goal to avoid uncoordinated construction. This work therefore focuses on an object oriented based classification in order to design its own methodological approach for the extraction of buildings and imprevious areas in selected areas of commercial suburbanization in the Prague hinterland. The aim is among others to find the classification rules for distinguishing different types of roofs and impervious areas depending on the material, shape characteristic etc. The literature overview summarizes methods of buildings and imprevious areas classification and extraction using very high resolution optical data and elevation data. Very high resolution QuickBird imagery and airbone laser scanning LIDAR elevation data and object oriented classification methods were used for and analysis of selected commercial suburbanization model areas in Prague hinterland. The proposed methodology uses...
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Extraction of Landscape Elements from Remote Sensing Data
Ferencz, Jakub ; Kalvoda, Petr (referee) ; Hanzl, Vlastimil (advisor)
This master thesis deals with a classification technique for an automatic detection of different land cover types from combination of high resolution imagery and LiDAR data sets. The main aim is to introduce additional post-processing method to commonly accessible quality data sets which can replace traditional mapping techniques for certain type of applications. Classification is the process of dividing the image into land cover categories which helps with continuous and up-to-date monitoring management. Nowadays, with all the technologies and software available, it is possible to replace traditional monitoring methods with more automated processes to generate accurate and cost-effective results. This project uses object-oriented image analysis (OBIA) to classify available data sets into five main land cover classes. The automate classification rule set providing overall accuracy of 88% of correctly classified land cover types was developed and evaluated in this research. Further, the transferability of developed approach was tested upon the same type of data sets within different study area with similar success – overall accuracy was 87%. Also the limitations found during the investigation procedure are discussed and brief further approach in this field is outlined.
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