iii ACKNOWLEDGEMENTS I would like to thank my advisor, Dr. Zhixiao Xie, for all of his support and advice over the last several years. I also want to thank Dr. for their support and ideas that helped improve this dissertation. I had a great time working with and learning from all of you, and you made this experience an enjoyable one. Finally, I would like to thank the Broward County Property Ap...

Little research has been conducted on how differing spatial resolutions or classification techniques affect image-driven identification and categorization of slum neighborhoods in developing nations. This study assesses the correlation between satellite-derived land cover and census-derived socioeconomic variables in Accra, Ghana to determine whether the relationship between these variables is ...

ses, the classification procedures generally used to produce a ~ ~ t i f i ~ i ~ l neural networks are attractive for use in the classiland-cover map are "hard" techniques which force allocation fication of land cover from remotely sensed data. In common to One class. Moreover, depending on the nature of the mixwith other classification approaches, artificial neural netture and its composite sp...

Using satellite imagery for the study of Earth's resources is attended by manyresearchers. In fact, the various phenomena have different spectral response inelectromagnetic radiation. One major application of satellite data is the classification ofland cover. In recent years, a number of classification algorithms have been developed forclassification of remote sensing data. One of the most nota...

An optimal Bayesian classifier using mixture distribution class models with joint learning of loss and prior probability functions is proposed for automatic land cover classification. The probability distribution for each land cover class is more realistically modeled as a population of Gaussian mixture densities. A novel two-stage learning algorithm is proposed to learn the Gaussian mixture mo...

The problem of generalizing spatial data is studied in the context of land cover data. The theory behind automatic generalization is reviewed and a raster based deterministic iterative generalization method is proposed. The methodology is based on the Map Algebra. The multivariate statistical testing methods are extended to deal also with generalized land cover data. The case study is related t...

A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classi...

-Land use classification is an important part of many remote-sensing applications. A lot of research has gone into the application of classifiers to remote-sensing images. Multi-spectral satellite imagery is an economical, precise and appropriate method of obtaining information on land use and land cover. In this paper, we have proposed an efficient technique for classifying the multispectral s...

A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts ...

Land cover classification requires both temporal and spatial information. Indeed, vegetation temporal evolution is necessary to discriminate the different land cover types. This information can be derived from coarse resolution sensors such as MERIS (300×300m pixel size), or SPOT/VGT (1km pixel size), whereas high resolution images, such as SPOT4/HRV ones (20×20m pixel size), contain the requir...