Mapping urban land cover using multi-scale and spatial autocorrelation information in high resolution imagery

• Other Authors: Johnson, Brian A.
• Format: Others
• Language: English
• Published: Florida Atlantic University
• Subjects: Image processing > Digital techniques; Remote sensing > Mathematics; Remote-sensing images; Computational intelligence; Cities and towns > Remote sensing; Environmental sciences > Remote sensing; Spatial analysis (Statistics)
• Online Access: http://purl.flvc.org/FAU/3342110

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 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. === by Brian A. Johnson. === Thesis (Ph.D.)--Florida Atlantic University, 2012. === Includes bibliography. === Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web.