Image Classification with a Region Based Approach in High Spatial Resolution Imagery

A number of studies have been carried out to find an appropriate spatial resolution to which to aggregate data in order to reduce the variation within an object, and minimize the classification error. Such approaches are pixel-based, and do not draw on the spatial variability as a source of information. The variability within an object can provide additional information that can be used for image classification. Instead of pixels, groups of pixels that form image segments, which are called “patches” in this study, were used for image classification. New methods that exploit multivariate statistics to improve the image classification are suggested. In the case of the object-based classification, patches are not expected to consist of pixels with completely homogeneous spectral radiances, but rather certain levels of variability are expected. To treat this variation within objects, multivariate normal distributions are assumed for every group of pixels in each patch, and multivariate variance-covariance matrices are calculated. A test of this approach was conducted using digital aerial imagery with a nominal one meter pixel size, and four multispectral bands, acquired over the small city of Morgantown, West Virginia, USA. Four classification methods were compared: the pixel-based ISODATA and maximum likelihood approaches, and region based maximum likelihood using patch means and patch probability density functions (pdfs). For region-based approaches, after initial segmentation, image patches were classified into seven classes: Building, Road, Forest, Lawn, Shadowed Vegetation, Water, and Shadow. Classification with ISODATA showed the lowest accuracy, a kappa index of 0.610. The highest accuracy, 0.783, was obtained from classification using the patch pdf. This classification also produced a visually pleasing product, with well-delineated objects and without the distracting salt-and-pepper effect of isolated misclassified pixels. The accuracies of classification with patch mean, and pixel based maximum likelihood were 0.735, 0.687 respectively.