Automatic building detection and land use classification in urban areas using multispectral high-spatial resolution imagery and LiDAR data

摘要

Urban areas areimportant environments, accounting for approximately half the population of theworld. Cities attract residents partly because they offer ample opportunitiesfor development, which often results in urban sprawl and its complex environmentalimplications. It is therefore necessary to develop technologies andmethodologies that permit monitoring the effects of various problems that havebeen or are thought to be associated with urban sprawl. These technologieswould facilitate the adoption of policies seeking to minimize the negativeeffects of urban sprawl. Solutions require a precise knowledge of the urbanenvironment under consideration to enable the development of more efficienturban zoning plans. The high dynamism of urban areas produces seeminglycontinuous alterations of land cover and use; consequently, cartographicinformation becomes quickly and is oftentimes outdated. Hence, the availabilityof detailed and up-to-date cartographic and geographic information is imperativefor an adequate management and planning of urban areas. Usually the process ofcreating land-use/land-cover maps of urban areas involves field visits andclassical photo-interpretation techniques employing aerial imagery. Thesemethodologies are expensive, time consuming, and also subjective. Digital imageprocessing techniques help reduce the volume of information that needs to bemanually interpreted. The aim of thisstudy is to establish a methodology to automatically detect buildings and toautomatically classify land use in urban environments using multispectralhigh-spatial resolution imagery and LiDAR data. These data were acquired in theframework of the Spanish National Plan for Airborne Orthophotographs, having beenavailable for public Spanish administrations.Two mainapproaches for automatic building detection and localization using high spatialresolution imagery and LiDAR data are evaluated The thresholding-based approachis founded on the establishment of two threshold values: one is the minimumheight to be considered as a building, defined using the LiDAR data; the other isthe presence of vegetation, defined with the spectral response. The otherapproach follows the standard scheme of object-based image classification:segmentation, feature extraction and selection, and classification, hereperformed using decision trees. In addition, the effect of including contextualrelations with shadows in the building detection process is evaluated. Qualityassessment is performed at both area and object levels. Area-level assessments evaluatethe building delineation performance whereas object-level assessments evaluatethe accuracy in the spatial location of individual buildings.Urban land-useclassification is achieved by applying object-based image analysis techniques.Objects are defined using the boundaries of cadastral plots. The plots were characterizedto achieve the classification by employing a descriptive feature setspecifically designed to describe urban environments. The proposed descriptivefeatures aim to emulate human cognition by numerically quantifying theproperties of the image elements and so enable each to be distinguishable.These features describe each plot as a single entity based on several aspectsthat reflect the information used: spectral, three-dimensional, and geometrictypologies. In addition, a set of contextual features at both the internal andexternal levels is defined. Internal context features describe an object withrespect to the land cover types contained within the plots, which were, in thiscase, buildings and vegetation. External context features characterise eachobject by considering the common properties of adjacent objects that, whencombined, create an aggregate in a higher level than plot level: urban blocks.Results show that thresholding-based building detection approachperforms better in the different scenarios assessed. This method produces amore accurate building delineation and object detection than the object-basedclassification method. The building type appears as a key factor in thebuilding detection performance. Thus, urban and industrial areas show betteraccuracies in detection metrics than suburban areas, due to the small size ofsuburban constructions, combined with the prominent presence of trees insuburban classes, hindering the building detection process. The relationsbetween buildings and shadows improve the object-level detection, removingsmall objects erroneously detected as buildings that negatively affect to thequality indices.Classificationtest results show that internal and external context features complement theimage-derived features, improving the classification accuracy values of urbanclasses, especially between classes that show similarities in their image-basedand three-dimensional features. Context features enable a superiordiscrimination of suburban building typologies, of planned urban areas andhistorical areas, and also of planned urban areas and isolated buildings.The outco