基于深度学习的自然灾害遥感影像语义分割

自然灾害种类繁多, 通过遥感影像语义分割相对比较困难. 为了能够更好实现遥感影像分割, 本文提出一种基于生成对抗网络的3层遥感影像语义分割模型, 针对不同场景的解析, 基于全卷积神经网络FCN, 设计一种多层次的遥感语义分割框架. 有效对遥感图像语义分割进行处理, 从而提高了模型的分割精度. 实验表明利用这种模型是有效的, 特别是受损建筑的分割结果, mIoU为82.28%, 通过该模型与其他网络模型进行对比, 其性能评价指标明显优于其他网络模型. 最后, 通过对自然灾害各种场景影像进行分析, 为应急管理部门提供一份可靠的数据报告.

Semantic Segmentation of Natural Disaster Remote Sensing Image Based on Deep Learning

There are many kinds of natural disasters, and it is relatively difficult to semantically segment remote sensing images. In order to better realize remote sensing image segmentation, this study proposes a three-layer semantic segmentation model for remote sensing images based on a generative adversarial network. For the analysis of different scenes, a multi-level remote-sensing semantic segmentation framework is designed based on a fully convolutional network (FCN). The semantic segmentation of remote sensing images is effectively performed, and thus the segmentation accuracy of the model is enhanced. Experiments show that this model is effective, which can be directly observed from the segmentation results of damaged buildings, with mIoU being 82.28 %. In addition, this model is compared with other network models, and its performance evaluation index is significantly better than that of other network models. Finally, a reliable data report is provided to emergency management departments by analyzing various scene images of natural disasters.