Combined multiscale segmentation convolutional neural network for rapid damage mapping from post earthquake very high-resolution images

Classifying land use from postearthquake very high-resolution (VHR) images is challenging due to the complexity of objects in Earth surface after an earthquake. Convolutional neural network (CNN) exhibits satisfied performance in differentiating complex postearthquake objects, thanks to its automatic extraction of high-level features and accurate identification of target geo-objects. Nevertheless, in view of the scale variance of natural objects, the fact that CNN suffers from the fixed receptive field, the reduced feature resolution, and the insufficient training sample has severely contributed to its limitation in the rapid damage mapping. Multiscale segmentation technique is considered as a promising solution as it can generate the homogenous regions and provide the boundary information. Therefore, we propose a combined multiscale segmentation convolutional neural network (CMSCNN) method for postearthquake VHR image classification. First, multiscale training samples are selected based on segments derived from the multiscale segmentation. Then, CNN is directly trained to classify the original image to further produce the preliminary classification maps. To enhance the localization accuracy, the output of CNN is further refined using multiscale segmentations from fine to coarse iteratively to obtain the multiscale classification maps. As a result, the combination strategy is able to capture objects and image context simultaneously. Experimental results show that the proposed CMSCNN method can reflect the multiscale information of complex scenes and obtain satisfied classification results for mapping postearthquake damage using VHR remote sensing images.