Feature-Fusion Segmentation Network for Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data

Abstract

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 hyperpixel-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 F1score metric is increased from 0.501 to 0.565.

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

“…Hyperspectral data have also begun to be used for deep learning landslide recognition [140]. (3) The feature segmentation of remote sensing images is also an important development direction for improving the accuracy of landslide identification [141,142]. An approach to fuse both local and non-local features can outperform state-of-the-art general-purpose semantic segmentation approaches [8].…”

“…A feature-based constraint deep U-Net (FCDU-Net) method to detect rainfall-induced mountainous landslides can achieve better landslide detection results than the other semantic segmentation methods [143]. A feature-fusion-based semantic segmentation network (FFS-Net) can extract texture and shape features from 2-D HRSIs, and terrain features taken from DEM data can greatly improve the segmentation accuracy of old, visually blurred landslides [141]. (4) The combination of deep learning and InSAR for early landslide prediction or reactivation identification of ancient landslides can achieve good prediction accuracy [141,[144][145][146][147][148][149][150][151].…”

Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey

“…Utilizing DSMs to extract additional features can further improve the semantic segmentation of multispectral images [228], [229], [232], [248], [233], [235], [236], [238]. In addition, other indicators such as Normalized DSM(NDSM) [230], [231], Digital Elevation Model (DEM) [123], [227], [237], Normalized Difference Vegetation Index (NDVI) [230], [231], [234] from the near-infrared and red channels, Normalized Difference Water Index [231] using near-infrared and green channels, are utilized to fuse with the multispectral images. [236], [239], [240].…”

“…Addition or Average [237], [230], [232]. This operation adds the feature maps element-wise or calculates the average mean of the feature maps to obtain fused feature.…”

Deep Evidential Remote Sensing Landslide Image Classification With a New Divergence, Multiscale Saliency and an Improved Three-Branched Fusion

“…The pixel-level landslide segmentation aims to identify the pixels (or sub-pixels) belonging to landslide areas using distinguishable visual features, while object-based methods group up adjacent pixels to enhance the utility of spatial distribution information [11]. Some studies also refer to segmentation-based scheme as landslide detection or landslide semantic classification [12]. In this realm, [13] associates attention mechanism with convolutional neural networks for optical remote sensing landslide segmentation.…”