Current Search:   Department of Geosciences (x) » Image processing (x)

View All Items

  • CSV Spreadsheet
(1 - 2 of 2)
Mapping urban land cover using multi-scale and spatial autocorrelation information in high resolution imagery
Title
Mapping urban land cover using multi-scale and spatial autocorrelation information in high resolution imagery.
Creator
Johnson, Brian A., Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description
Fine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of...
Show moreFine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of within-class spectral variability and between-class spectral similarity of many types of land cover leads to low classification accuracy when pixel-based, purely spectral classification techniques are used. Object-based classification methods, which involve segmenting an image into relatively homogeneous regions (i.e. image segments) prior to classification, have been shown to increase classification accuracy by incorporating the spectral (e.g. mean, standard deviation) and non-spectral (e.g. te xture, size, shape) information of image segments for classification. One difficulty with the object-based method, however, is that a segmentation parameter (or set of parameters), which determines the average size of segments (i.e. the segmentation scale), is difficult to choose. Some studies use one segmentation scale to segment and classify all types of land cover, while others use multiple scales due to the fact that different types of land cover typically vary in size. In this dissertation, two multi-scale object-based classification methods were developed and tested for classifying high resolution images of Deerfield Beach, FL and Houston, TX. These multi-scale methods achieved higher overall classification accuracies and Kappa coefficients than single-scale object-based classification methods., Since the two dissertation methods used an automated algorithm (Random Forest) for image classification, they are also less subjective and easier to apply to other study areas than most existing multi-scale object-based methods that rely on expert knowledge (i.e. decision rules developed based on detailed visual inspection of image segments) for classifying each type of land cover.
Show less
Date Issued
2012
PURL
http://purl.flvc.org/FAU/3342110
Subject Headings
Image processing, Digital techniques, Remote sensing, Mathematics, Remote-sensing images, Computational intelligence, Cities and towns, Remote sensing, Environmental sciences, Remote sensing, Spatial analysis (Statistics)
Format
Document (PDF)
From photo interpretation to GIS: Data quality assessments
Title
From photo interpretation to GIS: Data quality assessments.
Creator
Conaway, Michael D., Florida Atlantic University, Roberts, Charles, Shaw, Shih-Lung, Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description
Much of the recent research concerning the use of GIS has revolved around data quality. Types of errors inherent in GIS data layers, and also errors that may be produced through the creation and manipulation of data layers have been identified. Definitions of these errors, and observations of how these errors occur have been offered. However, the majority of the research is qualitative. It is known that positional variation is produced through differing interpretations and generalization of...
Show moreMuch of the recent research concerning the use of GIS has revolved around data quality. Types of errors inherent in GIS data layers, and also errors that may be produced through the creation and manipulation of data layers have been identified. Definitions of these errors, and observations of how these errors occur have been offered. However, the majority of the research is qualitative. It is known that positional variation is produced through differing interpretations and generalization of points, lines, and polygons, but it is not known to what extent. This information would be extremely helpful in allowing the user of the information to fine tune the application, based on the accuracy of the data. Providing this type of information is the goal of this research. Quantitative analysis of the results of a series of experiments will give a numerical range of possible positional errors produced through database creation via aerial photo interpretation.
Show less
Date Issued
1996
PURL
http://purl.flvc.org/fcla/dt/15280
Subject Headings
Geographic information systems, Remote sensing--Data processing, Image processing, Photographic interpretation
Format
Document (PDF)