Title :
Population density estimation using textons
Author :
Javed, Yousra ; Khan, Muhammad Murtaza ; Chanussot, Jocelyn
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci. (SEECS), Nat. Univ. of Sci. & Technol. (NUST), Islamabad, Pakistan
Abstract :
In this paper we propose an efficient method for population density estimation using textons and k nearest neighbor classifier (k-NN). Leung Malik (LM) filter bank is used for texture extraction (textons) from Google Earth Satellite Images and classification into high, medium, low population density and non-populated areas. We have tested the proposed method for 5 different images of cities of Pakistan at high resolution. Comparison of our results with those obtained using Grey Level Co-occurrence Matrix (GLCM) are also presented, indicating the effectiveness of the proposed method.
Keywords :
demography; feature extraction; geography; geophysical image processing; image classification; image texture; remote sensing; GLCM; Google Earth Satellite Images; Leung-Malik filter bank; Pakistan; grey level cooccurrence matrix; high population density areas; k nearest neighbor classifier; k-NN classifier; low population density areas; medium population density areas; nonpopulated areas; population density estimation; textons; texture extraction; Dictionaries; Earth; Filter banks; Google; Histograms; Sociology; Classification; Population Density; Textons;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
DOI :
10.1109/IGARSS.2012.6351062
