Automatic building extraction based on multiresolution segmentation using remote sensing data

Shrivastava, Neeti; Kumar Rai, Praveen

doi:0016-7282

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Automatic building extraction based on multiresolution segmentation using remote sensing data Shrivastava, NeetiKumar Rai, Praveen

Object structure

Automatic building extraction based on multiresolution segmentation using remote sensing data

Geographia Polonica Vol. 88 No. 3 (2015)

Shrivastava, Neeti ; Kumar Rai, Praveen

building extraction ; high resolution data ; multiresolution segmentation ; object orientede fuzzy classification ; spatial filter

Analysis of high resolution remote sensing images, included in the object-oriented approach, involved classifying the image objects according to class descriptions organised in an appropriate knowledge base. This technique is created by means of inheritance mechanisms, concepts, and methods of fuzzy logic and semantic modeling. The process of the object oriented classification mainly involved two sections: multiresolution segmentation and image classification. Multiresolution segmentation is a new procedure for image object extraction. It allows the segmentation of an image into a network of homogeneous image regions at any chosen resolution. These image object primitives represent image information in an abstract form, serving as building blocks and information carries for subsequent classification. A study was taken up to perform object oriented fuzzy classification using high resolution satellite data (Cartosat-1 fused with IRS-1C, LISS IV data) for automatic building extraction in the study area covering the administrative area of BHEL (Bharat Heavy Electrical Limited) colony, Haridwar, Uttrakhand (India). The study area was located at 29°56’55.51”N to 29°56’11.49”N latitude and 78°05’42.45”E to 78°07’00.09”E longitude. Two approaches were used: applying different spatial filters, and object orientation. The merged image is filtered using different high pass filters, such as: Kirsch, Laplace, Prewitt, Sobel, and Canny filtered images. The overall accuracy of the classified image was 0.93, and Kappa accuracy was 0.89. The produced accuracy for buildings, vegetation, and shadows were 0.9545, 1.0, and 0.8888, respectively, whereas user accuracy for buildings vegetation, and shadows were 1.0, 0.9375, and 1.0, respectively. Overall classification accuracy was based on TTA mask (training and test area mask) and it was 0.97. Kappa accuracy was 0.95.

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