Pattern-based regionalization of large geospatial datasets using complex object-based image analysis

Abstract

Pattern-based regionalization - spatial classification of an image into sub-regions characterized by relatively stationary patterns of pixel values - is of significant interest for conservation, planing, as well as for academic research. A technique called the complex object-based image analysis (COBIA) is particularly well-suited for pattern-based regionalization of very large spatial datasets. In COBIA image is subdivided into a regular grid of local blocks of pixels (complex objects) at minimal computational cost. Further analysis is performed on those blocks which represent local patterns of pixel-based variable. A variant of COBIA presented here works on pixel-classified images, uses a histogram of co-occurrence pattern features as block attribute, and utilizes the Jensen-Shannon divergence to measure a distance between any two local patterns. In this paper the COBIA concept is utilized for unsupervised regionalization of land cover dataset (pixel-classified Landsat images) into landscape types - characteristic patterns of different land covers. This exploratory technique identifies and delineates landscape types using a combination of segmentation of a grid of local patterns with clustering of the segments. A test site with 3.5 × 108 pixels is regionalized in just few minutes using a standard desktop computer. Computational efficiency of presented approach allows for carrying out regionalizations of various high resolution spatial datasets on continental or global scales.