A New Scheme for Land Cover Classification in Aerial Images: Combining Extended Dependency Tree-HMM and Unsupervised Segmentation

International audienceAn important challenge to any image pixels classification system is to correctly assign each pixel to its proper class without blurring edges delimiting neighboring regions. In this paper, we present an aerial image mapping approach that advantageously combines unsupervised segmentation with a supervised Markov model based recognition. The originality of the proposed system carries on three concepts: the introduction of an auto-adaptive circular-like window size while applying our stochastic classification to preserve region edges, the extension of the Dependency Tree-HMM to permit the computation of likelihood probability on windows of different shapes and sizes and a mechanism that checks the coherence of the indexing by integrating both segmentations results: from unsupervised over segmentation, regions are assigned to the predominating class with a focus on inner region pixels. To validate our approach, we achieved experiments on real world high resolution aerial images. The obtained results outperform those obtained by supervised classification alone

MULTISCALE AND MULTITEMPORAL URBAN REMOTE SENSING

The remote sensing of urban areas has received much attention from scientists conducting studies on measuring sprawl, congestion, pollution, poverty, and environmental encroachment. Yet much of the research is case and data-specific where results are greatly influenced by prevailing local conditions. There seems to be a lack of epistemological links between remote sensing and conventional theoretical urban geography; in other words, an oversight for the appreciation of how urban theory fuels urban change and how urban change is measured by remotely sensed data. This paper explores basic urban theories such as centrality, mobility, materiality, nature, public space, consumption, segregation and exclusion, and how they can be measured by remote sensing sources. In particular, the link between structure (tangible objects) and function (intangible or immaterial behavior) is addressed as the theory that supports the wellknow contrast between land cover and land use classification from remotely sensed data. The paper then couches these urban theories and contributions from urban remote sensing within two analytical fields. The first is the search for an "appropriate" spatial scale of analysis, which is conveniently divided between micro and macro urban remote sensing for measuring urban structure, understanding urban processes, and perhaps contributions to urban theory at a variety of scales of analysis. The second is on the existence of a temporal lag between materiality of urban objects and the planning process that approved their construction, specifically how time-dependence in urban structural-functional models produce temporal lags that alter the causal links between societal and political functional demands and structural ramifications

Morphology from imagery: detecting and measuring the density of urban land use

Defining urban morphology in terms of the shape and density of urban land use has hitherto depended upon the informed yet subjective recognition of patterns consistent with spatial theory. In this paper we exploit the potential of urban image analysis from remotely sensed data to detect, then measure, various elements of urban form and its land use, thus providing a basis for consistent definition and thence comparison. First, we introduce methods for classifying urban areas and individual land uses from remotely sensed images by using conventional maximum likelihood discriminators which utilize the spectral densities associated with different elements of the image. As a benchmark to our classifications, we use smoothed UK Population Census data. From the analysis we then extract various definitions of the urban area and its distinct land uses which we represent in terms of binary surfaces arrayed on fine grids with resolutions of approximately 20 m and 30 m. These images form surfaces which reveal both the shape of land use and its density in terms of the amount of urban space filled, and these provide the data for subsequent density analysis. This analysis is based upon fractal theory in which densities of occupancy at different distances from fixed points are modeled by means of power functions. We illustrate this for land use in Bristol, England, extracted from Landsat TM-S and SPOT HRV images and dimensioned from population census data for 1981 and 1991. We provide for the first time, not only fractal measurements of the density of different land uses but measures of the temporal change in these densities.

Evaluation of gridded population models using 2001 Northern Ireland Census data

There is growing interest in the use of gridded population models which potentially offer advantages of stability through time and ease of integration with nonpopulation data sources. This paper assesses the accuracy of models of the type introduced by Martin in 1989. Population counts for census output areas (OAs) are reallocated to a 100 m grid and then compared with true 100 m cell population counts uniquely available from the 2001 Northern Ireland Census. This analysis is novel, being the first large-scale assessment of gridded population models against true gridded population counts. We find evidence that kernel width and cell size are more important than the distance-decay parameter; that local mass preservation approaches are more appropriate in urban areas; but that the spatial scale of input data is more important than model parameters. It is suggested that more attention needs to be given to the varying spatial structures of population between places and that incorporating this information through geostatistical approaches could yield further insights. <br/