Huge challenges exist for old landslide detection because their morphology features have been partially or strongly transformed over a long time and have little difference from their surrounding. Besides, small-sample problem also restrict in-depth learning. In this paper, an iterative classification and semantic segmentation network (ICSSN) is developed, which can greatly enhance both object-level and pixel-level classification performance by iteratively upgrading the feature extractor shared by two network. An object-level contrastive learning (OCL) strategy is employed in the object classification sub-network featuring a siamese network to realize the global features extraction, and a sub-object-level contrastive learning (SOCL) paradigm is designed in the semantic segmentation sub-network to efficiently extract salient features from boundaries of landslides. Moreover, an iterative training strategy is elaborated to fuse features in semantic space such that both object-level and pixel-level classification performance are improved. The proposed ICSSN is evaluated on the real landslide data set, and the experimental results show that ICSSN can greatly improve the classification and segmentation accuracy of old landslide detection. For the semantic segmentation task, compared to the baseline, the F1 score increases from 0.5054 to 0.5448, the mIoU improves from 0.6405 to 0.6610, the landslide IoU improved from 0.3381 to 0.3743, and the object-level detection accuracy of old landslides is enhanced from 0.55 to 0.9. For the object classification task, the F1 score increases from 0.8846 to 0.9230, and the accuracy score is up from 0.8375 to 0.8875.
翻译:旧的山体滑坡探测存在巨大的挑战,因为其形态特征长期以来已经部分或强烈地转变,与周围没有什么区别。此外,小型模版问题也限制深入学习。在本文件中,开发了迭代分类和语义分解网络(ICSSN),通过迭代更新两个网络共享的地物提取器,可以大大提高物体水平和像素等级分类性能。在目标分类子网络中使用了目标级对比学习(OCL)战略,该子网络拥有一个Siamee网络,以实现全球目的提取,而小目标级的精确度水平对比学习(SOCL)模式也限制深入学习。在本文件中,开发了一个迭代分类和语义分解网络(ICSSN),通过迭代更新两个网络共享地貌提取器的特性。 拟议的ICSSNSSN在真实地滑坡数据集上进行了评估,实验结果显示ICSSNS从0.8、0.8级和0.8级的精确度测量值(SOCLI),从0.8至0.5级的更精确度分析到更精确度, 改进了旧的平地段的平地段的Selev1到0.5。</s>