Building Damage Assessment Based on CNN Transfer Learning
XU Jinkang
TIAN Jinwen
Abstract：Detection of damaged buildings from remote sensing images is important for earthquake emergency response and quick relief.In most cases,only post-event images are available,damage assessment becomes a question of image classification.In this paper,a method which combines object-oriented image classification and CNN transfer learning is proposed. In the transfer learning algorithm based on convolution neural network,this paper uses the feature extraction method based on spatial pooling,and proposes an improved idea of multi-window fusion for the problem that the window scale cannot be set automatically.The experimen-tal results show that the proposed feature based on the transfer learning is more practical than the traditional manual construction. Based on the multi-window fusion,it can avoid the problem of setting the sampling window and greatly increase the engineering practicability of the algorithm.
Keywords：image classificationdamage assessmenttransfer learning
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 677-681 )