A hierarchical object-based approach for urban land-use classification from remote sensing data (2003)

Land-cover and land-use data are essential for urban planning and management.Traditional land-use mapping by visual image-interpretation is expensive, time-consuming and often subjective.Researchers have been searching for automatic or semi-automatic approaches for many years.The combination of airborne LIDAR data with high spatial resolution and multi-spectral images such as IKONOS, QuickBird and SPOT 5 offers great opportunities, especially for application in urban areas.The second generation of airborne scanners with the capacity to acquire simultaneously range and multi-spectral intensity data, makes it possible to extract many meaningful features for land-use classification.The overall objective of this research is the development of a semi-automatic approach for land-cover and land-use classification, based on laser scanning data and multi-spectral images and the development of methods for the consistent aggregation of elementary objects to composite objects at higher abstraction levels.In this research, several new sensor data have been used and examined for urban land-cover and land-use classification.We have taken the most popular pixel-based classifier, the maximum likelihood classifier (MLC), as an example of traditional classifiers and applied it to highresolution data.A number of problems have been observed and highlighted, and several remedial measures have been proposed and tested.Land-cover classification accuracy can be improved by modelling the decision surface in the feature space and by selecting samples from both pure pixels and mixed pixels.Spatial partitioning of decision surfaces based on samples of end-member classes is the key to the proposed solutions.The experimental results have confirmed the effectiveness of the proposed class integration method, which uses pure and mixed samples.Despite the improved land-cover classification accuracy of MLC, we consider the attainable results insufficient for a detailed urban land-use classification.The key features for image-interpretation (size, shape, colour, orientation, pattern, association) are characteristics for certain iii Summary types of objects and are only relevant for abstraction levels higher than the pixel level.These features play a key role in image analysis and land-use classification.Object-based image processing techniques are considered for image analysis at a supra pixel level.Therefore, an objectbased image analysis approach has become the main focus of this research.This research develops a hierarchical object-based approach for urban land-use classification.The proposed method consists of three steps: land-cover classification, the definition and delineation of land-use units and land-use classification.It incorporates pixel-based image processing techniques and object-based techniques at different stages.Various techniques have been proposed and tested for object extraction at different aggregation levels.Several concepts and methods have been proposed and discussed to extract image objects and object properties, and to identify explicit topological relations between image objects.We have elaborated and applied the hybrid-raster data model to explicitly identify topological relationships between image objects.We have tested these concepts and methods for urban land-cover and land-use classification on two test sites.The test results demonstrate the effectiveness of the proposed per-object approach.Structural information derived from hierarchical image objects plays an important role in land-use classification of urban areas.Delaunay triangulation has been successfully applied to spatially disjoint objects to obtain spatial adjacency relationships and proximity measures; these provide essential information for spatial clustering of objects that form spatial land-use units.Several measures have been proposed and tested for the evaluation of the similarity of buildings.These similarity measures in combination with the spatial adjacency relationships and proximity measures provide information for spatial clustering of land-cover objects, which form spatial land-use units.Several object properties have been proposed and extracted as attributes of land-use objects for our two test sites.Fuzzy membership functions have been designed to establish the relationships between extracted land-use object properties and designated land-use classes.A fuzzy classifier has been applied for per-object classification based on extracted land-use units and their object properties.The obtained results show that the proposed object-based land-use classification approach is promising.The extracted properties of land-use objects are also important information for urban studies, planning and management.A united framework for quality assessment has been proposed and tested, based on similarity measures between classified data and reference data.This utilises per-object and per-pixel measurements.The proposed per-object quality measures provide possibilities for obtaining additional quality assessment based on various object properties.The proposed uncertainty measures for extracted land-cover objects and iv Summary classified land-use objects have been tested, and we expect them useful in controlling the classification process.The developed concepts and methods have been implemented by programming in Matlab.The implemented system allows different users to specify characteristics of information that need to be extracted from laser data and spectral data, in order to obtain the desired results.This feature offers planners and other users the opportunity to produce results according to their specific wishes and application requirements from a detailed data set.The multi use of such detailed data sets is important because of the relative high costs of acquiring high-resolution laser data and spectral data.The experimental results show the relevance of hierarchical object modelling in combination with structural image analysis techniques for urban land-cover and land-use classification.Buildings, green spaces, water surfaces and sealed-ground surfaces have been successfully extracted at the land-cover level.Spatial landuse units have been obtained by aggregation of the extracted land-cover objects.The high quality of the per-object land-use classification has been established by comparing it with the results of visual interpretation.