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首页> 外文期刊>Journal of Applied Remote Sensing >Combined multiscale segmentation convolutional neural network for rapid damage mapping from postearthquake very high-resolution images
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Combined multiscale segmentation convolutional neural network for rapid damage mapping from postearthquake very high-resolution images

机译：组合多尺度分割卷积神经网络，用于从骨折超高分辨率图像中的快速损伤映射

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摘要

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. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译：根据地震后的物体在地面物体的复杂性，分类来自Postearthquake的土地利用非常高分辨率（VHR）图像是挑战。卷积神经网络（CNN）在鉴定复杂的凹陷物体方面表现出满意的性能，因为它的自动提取高级特征和准确识别目标地理对象。然而，鉴于自然对象的规模方差，CNN遭受固定接收领域的事实，降低的特征分辨率和不足的训练样本严重导致其在快速损伤映射中的限制。多尺度分割技术被认为是有希望的解决方案，因为它可以产生均匀区域并提供边界信息。因此，我们提出了一种组合的多尺度分割卷积神经网络（CMSCNN）方法，用于追溯VHR图像分类。首先，基于来自多尺度分割的段选择多尺度训练样本。然后，直接培训CNN以对原始图像进行分类，以进一步产生初步分类映射。为了提高本地化精度，CNN的输出进一步使用多尺度分段来精制，从精细分割到迭代地粗糙以获得多尺度分类映射。结果，组合策略能够同时捕获对象和图像上下文。实验结果表明，所提出的CMSCN方法可以反映复杂场景的多尺度信息，并获得使用VHR遥感图像映射底震损伤的满意分类结果。 （c）2019年光学仪表工程师协会（SPIE）

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