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.

本文提出了一种迭代分类和语义分割网络 (ICSSN) 方法，通过迭代升级两个网络共享的特征提取器，大大增强了对象级和像素级分类性能，并在对象分类子网络中采用物体级对比学习 (OCL) 策略和在语义分割子网络中设计子目标级对比学习 (SOCL) 范例来提取边界的显著特征。在实验数据集上测试表明，该网络可大幅提高老滑坡检测的分类和分割准确性。

一种基于高分辨率遥感图像的旧滑坡检测迭代分类和语义分割网络