Land Use Classification Using Conditional Random Fields for the Verification of Geospatial Databases

Abstract

Geospatial land use databases contain important information with high benefit for several users, especially when they provide a detailed description on parcel level. Due to many changes connected with a high effort of the update process, these large-scale land use maps become outdated quickly. This paper presents a two-step approach for the automatic verification of land use objects of a geospatial database using high-resolution aerial images. In the first step, a precise pixel-based land cover classification using spectral, textural and three-dimensional features is applied. In the second step, an object-based land use classification follows, which is based on features derived from the pixel-based land cover classification as well as geometrical, spectral and textural features. For both steps, the potential of the incorporation of contextual knowledge in the classification process is explored. For this purpose, we use Conditional Random Fields (CRF), which have proven to be a flexible, powerful framework for contextual classification in various applications in remote sensing. The results of the approach are evaluated on an urban test site and the influence of different features and models on the classification accuracy is analysed. It is shown that the use of CRF for the land cover classification yields an improved accuracy and smoother results compared to independent pixel-based approaches. The integration of contextual knowledge also has a remarkable positive effect on the results of the land use classification.