Object-Oriented Analysis of Satellite Images Using Artificial Neural Networks for Post-Earthquake Buildings Change Detection

Abstract

ABSTRACT:Earthquake is one of the most divesting natural events that threaten human life during history. After the earthquake, having information about the damaged area, the amount and type of damage can be a great help in the relief and reconstruction for disaster managers. It is very important that these measures should be taken immediately after the earthquake because any negligence could be more criminal losses. The purpose of this paper is to propose and implement an automatic approach for mapping destructed buildings after an earthquake using pre-and post-event high resolution satellite images. In the proposed method after preprocessing, segmentation of both images is performed using multi-resolution segmentation technique. Then, the segmentation results are intersected with ArcGIS to obtain equal image objects on both images. After that, appropriate textural features, which make a better difference between changed or unchanged areas, are calculated for all the image objects. Finally, subtracting the extracted textural features from pre-and post-event images, obtained values are applied as an input feature vector in an artificial neural network for classifying the area into two classes of changed and unchanged areas. The proposed method was evaluated using WorldView2 satellite images, acquired before and after the 2010 Haiti earthquake. The reported overall accuracy of 93% proved the ability of the proposed method for post-earthquake buildings change detection.

Building Change Detection Based on a Gray-Level Co-Occurrence Matrix and Artificial Neural Networks

“…Learning curves are a widely used diagnostic tool for evaluating model learning performance over experience, i.e., as the model is exposed to more and more training data, as well as for identifying problems, such as model underfitting or overfitting. According to this figure, the developed ANN model shows an MSE value equal to 0.078 at the convergence point, which is lower than Zahraee and Rastiveis[50], even though it reached the best validation performance after almost the same number of training epochs, i.e., at epoch 18, as in the aforementioned study.…”

“…In [23], the recovery of the Italian city of L'Aquila following the 2009 earthquake was monitored by using spectral, textural and geometric features in order to determine changes in buildings, thus allowing a reduction in the extensive fieldwork required. The Haiti 2010 earthquake was also studied in [50], where authors mapped the damaged buildings by developing an ANN model for classifying the affected area into two classes of changed and unchanged areas, using the extracted textural features calculated using the GLCM from pre-and post-event images as an input vector. The ability of the proposed method to detect building changes was proved by the reported overall accuracy of 93%.…”