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Combined Deep Learning and Multiscale Segmentation for Rapid High Resolution Damage Mapping

High resolution seismic remote sensing image classification plays a key role in rapid post-earthquake damage assessment and emergency rescue. Convolution neural networks (CNNs) exhibit good performance in image classification, especially for automatic extraction of high-level features and accurate identification of target objects. However, it suffers from difficulty in localization accuracy due to the reduced feature resolution in the repeated combination of max-pooling and downsampling in the network, which may lead to poor objects delineation. To tackle this problem, multiscale segmentation is considered as a promising solution. It extracts the objects information based on the regions contained in the multiscale segmentation results, so that the original boundary information can be well-preserved. In this paper, we propose a combined convolutional neural network and multiscale segmentation (CMSCNN) method for high resolution seismic image classification. First, CNN is directly trained to classify the original image. The initial classification map is combined with the multiscale segmentation results to obtain the class-based segmented images with different scales. Especially, the multiscale segmentation considers the scale variance of different objects, thus capturing objects as well as image context at the same time. Finally, these classification images are voted to determine the final results of segmented images in classes. The experimental results show that the proposed approach can reflect the multiscale information of complex scenes and obtain good classification results for mapping seismic damage using high resolution remote sensing images.