Damaged Building Detection in the crisis areas using Image Processing Tools
Swaminaidu.G1, Soundarya Mala.P2, Sailaja.V3
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This paper describes the how to detect the damaged buildings in remotely sensed areas. There are several different algorithms for automated change detection. These methods are based on isotropic frequency filtering, spectral and texture analysis, and segmentation. Texture is a sample area, it defines properties of surface. Texture is an important approach to region description. Sometimes edge detection cannot identify the changes or damages in the buildings, in that type of situations the texture analysis is most useful. The three principal approaches used in image processing to describe the texture of a region are statistical, structural and spectral. Texture analysis gives the more reliable to detect the damaged buildings in crisis areas. For the texture analysis, we calculate the Haralick properties parameters such as energy and homogeneity for the images. A rule-based combination of the change algorithms is applied to calculate the probability of change for a particular location. Now a day’s damaged building detection using image processing techniques occupy prominent place. Texture properties play a vital role in the damaged building detection. The method proposed in this paper used texture features for the detection of damaged buildings in the crisis areas.