A texture-based classification method for classifying built areas according to their density

Abstract

Quantitative information about building density can serve as a useful tool for urban applications, such as the study of urban land changes, illegal building development, urban expansion, etc. This paper presents a method that classifies built areas according to their density into three categories using SPOT panchromatic remote sensing images. It is based on the concept of statistical measurement of texture. Specifically, three algorithms, which employ occurrence frequency or co-ocurrence matrices concepts on binary data, were developed, tested over the broader Athens area in Attica and evaluated. The developed algorithms were equally effective when windows larger than 31 x 31 pixels were used. For such windows, the overall accuracy of the method ranges from 83.40 to 89.61%. These results are better than the 79.70% that was obtained using the maximum likelihood classifier. The kappahat coefficient was also improved by about 0.16 units, while the accuracy of the classification for each class of built areas was improved by about 50-60%.