Urban area and building detection on high resolution multispectral satellite images using spatial statistics

With the increase in the resolution and the amount of satellite images, automatic extraction of urban areas and buildings became more important in the past decade. Extracting such information manually is tedious and needs a lot of expert effort. In this work, a system for detecting the urban areas, then finding the buildings inside these areas is proposed. LISA analysis is used for detection of urban areas. After the urban area is detected, a mean-shift based segmentation is applied; then each segment is decided as building or not by using segment-test on spectral features and Local Moran's I value. Classification of buildings is done by KNN (K-Nearest Neighbor) classifier and Parzen classifiers. Input images to be used are 3 band multispectral images.

[1]  Cem Ünsalan,et al.  Urban Area Detection Using Local Feature Points and Spatial Voting , 2010, IEEE Geoscience and Remote Sensing Letters.

[3]  Nina S. N. Lam,et al.  A comparison of local variance, fractal dimension, and Moran's I as aids to multispectral image classification , 2005 .

[4]  Rasmus Fensholt,et al.  Remote Sensing , 2008, Encyclopedia of GIS.

[5]  Jon Atli Benediktsson,et al.  Classification and feature extraction for remote sensing images from urban areas based on morphological transformations , 2003, IEEE Trans. Geosci. Remote. Sens..

[6]  Wei Su,et al.  Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery , 2008 .

[7]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Cem Ünsalan,et al.  Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[9]  L. Anselin.  Local Indicators of Spatial Association—LISA , 2010 .

[10]  P. Moran.  Notes on continuous stochastic phenomena. , 1950, Biometrika.

[11]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[12]  Haiqing Xu,et al.  Urban building damage detection from very high resolution imagery using one-class SVM and spatial relations , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[13]  V. Karathanassi,et al.  A texture-based classification method for classifying built areas according to their density , 2000 .

[14]  Cem Unsalan,et al.  Urban area detection using gabor features and spatial voting , 2009, 2009 IEEE 17th Signal Processing and Communications Applications Conference.