Measuring Land Development in Urban Regions Using Graph Theoretical and Conditional Statistical Features

Inferring land use from satellite images is extensively studied by the remote sensing and pattern recognition communities. In previous studies, the focus was on classifying large regions due to the resolution of available satellite images. Nowadays, very high-resolution satellite imagery (Ikonos and Quickbird) allows researchers to focus on more complex land-use problems such as monitoring development in urban regions. Solutions to these complex problems may improve the life standards of city residents. To this end, we focus on automatically monitoring construction zones using their very high-resolution panchromatic satellite images through time. To monitor land development, we obtain sequential images of a selected region. Then, we extract features from each image in the sequence. Comparing values of these features, we expect to measure the degree of land development through time. In a similar study, we introduced graph theoretical measures over Ikonos imagery to measure organization in a given satellite image. This paper is an extension of our previous work with more powerful new features. Here, we first introduce a novel method to extract straight line segments using a least squares ellipse fitting. Then, we introduce four new graph theoretical features. More importantly, we introduce a novel method to embed the spatial information in gray-level co-occurrence matrix statistical features to measure land development. Finally, we test all our existing and new features to measure land development in 19 different urban construction zones. Our test set consists of Ikonos satellite images of these regions captured in separate times.