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：古滑坡检测面临巨大挑战，因其形态特征经过长期演化已部分或强烈改变，与周围环境差异甚微。此外，小样本问题也制约了深度学习的发展。本文提出一种迭代分类与语义分割网络（ICSSN），通过对双网络共享的特征提取器进行迭代优化，显著提升目标级与像素级分类性能。在目标分类子网络中采用目标级对比学习（OCL）策略，构建孪生网络实现全局特征提取；在语义分割子网络中设计子目标级对比学习（SOCL）范式，高效提取滑坡边界显著性特征。同时，本文创新性地设计了迭代训练策略，通过融合语义空间特征，同步改善目标级与像素级分类性能。基于真实滑坡数据集的实验结果表明：ICSSN能大幅提升古滑坡检测的分类与分割精度。在语义分割任务中，F1分数从0.5054提升至0.5448，mIoU从0.6405提升至0.6610，滑坡IoU从0.3381提升至0.3743，古滑坡目标级检测精度从0.55提升至0.9；在目标分类任务中，F1分数从0.8846提升至0.9230，准确率从0.8375提升至0.8875。