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EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:

          A detailed investigation of the characteristics of the areas damaged due to the Bam earthquake in terms of the differences in the backscattering coefficient and the correlation coefficient of the pre- and post-event Envisat/ASAR images was conducted in order to raise the precision of damage detection.

          Finally, the damage-mapping scheme was revised to present the distribution of damaged areas in Bam.

PROPOSED CONCEPT:

          An unsupervised method is presented to detect earthquake-triggered “roof-holes” on rural houses from unmanned aerial vehicle (UAV) images. First, both orthomosaic and gradient images are generated from a set of UAV images.

          Finally, “roof-holes” on rural houses are detected using the learned model.

EXISTING  TECHNIQUE :

          A DAMAGE DETECTION METHOD

PROPOSED ALGORITHM:

          ROOF-HOLE DETECTION

TECHNIQUE DEFINITION:

          Two multi-looked intensity images taken before and after an earthquake were prepared. It is desirable that the acquisition dates are as close as possible to the day of the earthquake and that the observation conditions are similar. However, the method was successful in damage detection for the Kobe example, although the image pair had vastly different observation orbits before and after the earthquake. After co-registering the pre- and post-event images, each image was filtered using a Lee filter with a 21_21 pixel window.

ALGORITHM DEFINITION:

          It is done by comparing the distribution of each segment in the clustering result and distribution of “roof-hole” segments in the ground truth. The similarity between two distributions is calculated based on Kullback–Leibler (KL) divergence. For each “roof-hole” segment in the ground truth, all of the segments in the clustering result can be sorted according to their scores computed byKLdivergence. Then, themost representative segments with high scores can be selected as the detected roof-holes.

DRAWBACKS:

          The detection of earthquake by detecting the collapsed building didn’t gave us accuracy due to some surface problems in that method.

          The detection based on shadows is having some drawbacks, because it requires supervision regularly.

           In the existing methods there is no unsupervised earthquake detection system.

ADVANTAGES:

          Many of the myriad tactical uses of EEW - stopping surgery, trains, airport take-offs and landing, closing vulnerable bridges, opening critical doors, warning school kids, and the population in general, and quick tsunami warning input - will prove valuable.

           The reassurance that the populace does not need to interpret every bump in the night as potentially apocalyptic, i.e., that most noticeable earthquakes are not going to be damaging.