As a harzard disaster, landslide often brings tremendous losses to humanity,
so it's necessary to achieve reliable detection of landslide. However, the
problems of visual blur and small-sized dataset cause great challenges for old
landslide detection task when using remote sensing data. To reliably extract
semantic features, a hyper-pixel-wise contrastive learning augmented
segmentation network (HPCL-Net) is proposed, which augments the local salient
feature extraction from the boundaries of landslides through HPCL and fuses the
heterogeneous infromation in the semantic space from High-Resolution Remote
Sensing Images and Digital Elevation Model Data data. For full utilization of
the precious samples, a global hyper-pixel-wise sample pair queues-based
contrastive learning method, which includes the construction of global queues
that store hyper-pixel-wise samples and the updating scheme of a momentum
encoder, is developed, reliably enhancing the extraction ability of semantic
features. The proposed HPCL-Net is evaluated on a Loess Plateau old landslide
dataset and experiment results show that the model greatly improves the
reliablity of old landslide detection compared to the previous old landslide
segmentation model, where mIoU metric is increased from 0.620 to 0.651,
Landslide IoU metric is increased from 0.334 to 0.394 and F1-score metric is
increased from 0.501 to 0.565.

通过引入高像素对比学习增强分割网络（HPCL-Net）来可靠提取语义特征，从而加强了原地滑坡分割模型的可靠性。在一个黄土高原的老地滑数据集上的实验证明，与之前的模型相比，该模型显著提高了原地滑坡的检测准确率。

一种基于高分辨率遥感影像和数字高程模型数据的超像素对比学习增强的旧滑坡检测分割网络

A Hyper-pixel-wise Contrastive Learning Augmented Segmentation Network  for Old Landslide Detection Using High-Resolution Remote Sensing Images and  Digital Elevation Model Data