Lacunarity based measures can be described as texture recognition approaches that provide a flexible yet theoretically consistent mean of characterizing the morphology of urban spatial patterns across different scales. This paper proposes a comparison between the Gliding-Box and Differential Box-Counting algorithms based on the concept of lacunarity to recognize and classify textures of urban areas with different inhabitability conditions through the analysis of binary and grayscale images from 30 ® Quickbird sensor image samples from Recife (Brazil), captured in October 2001, with 250 x 250 meters in size. Results show that the Differential Box Counting algorithm applied in grayscale images improves the discrimination between textures from urban areas with different inhabitability conditions, and it reveals a strong correlation between urban morphology and socioeconomic patterns.
More details are described on the paper
Accuracy of lacunarity algorithms in texture classification of high spatial resolution images from urban areas which was published on the annals of the XXI Congress of the International Society for Photogrametry and Remote Sensing-
ISPRS, held on 3-11 July 2008, in Beijing, China.
