Using local features to measure land development in urban regions
Monitoring urban development in a given region provides valuable information to researchers. Currently available, very high resolution satellite images can be used for this purpose. However, manually monitoring land development using these large and complex images is time consuming and prone to errors. To handle this problem, an automated system is needed to measure development in urban regions. Therefore, in this study we propose such an automated method to measure land development in a given urban region imaged in different times. We benefit from novel land development measures for this purpose. They are based on local features obtained from sequential images. As a novel contribution, we represent these local features in a spatial voting matrix. Then, we propose five different land development measures on the formed voting matrix. We test our method on 19 sets of sequential panchromatic Ikonos images. Our test results indicate the possible use of our method in measuring land development automatically.
local features measuring land development spatial voting ikonos images gabor filtering