AED-Net: Semantic Segmentation Model for Landslide Recognition from Remote Sensing Images

Remote sensing images contain rich semantic information and play an important role in landslide disaster monitoring. Traditional landslide recognition is mainly based on remote sensing visual interpretation and human- computer interaction recognition, which is time and labor consuming, with strong subjectivity and low extraction accuracy. Semantic segmentation, as an important task in deep learning, has played an important role in automatic recognition tasks using remote sensing images due to its end- to- end, pixel- level classification capability and has great potential in automatic recognition of landslides. The existing semantic segmentation models for landslides using remote sensing images usually lack the feature information of multi- scale ground objects, and the boundary will be blurred with the increase of network depth. In this paper, Attention combined with Encoder- Decoder Network (AED- Net) is proposed for landslide recognition. A shallow feature extraction network is used to alleviate the boundary ambiguity caused by deep neural network. Multi- scale feature extraction capability of convolution pool pyramid structure in void space is utilized. Combined with the feature restoring ability of the encoder-decoder structure, the boundary information is restored, and the channel attention mechanism is used to enhance the key feature learning ability of the model. The focal- loss function is used to alleviate the imbalance of positive and negative samples. In our study, firstly, the GID- 5 data set is used to conduct comparative tests on the expansion rate setting of void convolutions and the selection of channel attention mechanism in the model to get the optimal solution. Then, the feature weight is transferred to the semantic segmentation task for landslide disaster by using transfer learning method, and the hyperparameter discussion and ablation experiment are carried out. The resulting model achieves the optimal segmentation performance on the landslide disaster data set of Bijie City, with a Pixel Accuracy (PA) of 95.58%, the Mean Pixel Accuracy (MPA) of 89.24%, and the Mean Intersection over Union (MIoU) of 82.68% . Compared with classical semantic segmentation networks such as PSP- Net, Attention U- Net, DeeplabV3 + with ECA attention mechanism, and semantic segmentation models such as PA- Fov and LandsNet for classfifying landslide disasters, the pixel accuracy of our model increases by 0.73% ~1.97% . The average pixel accuracy of all categories increases by 1.0%~2.84%, and the average interaction ratio increases by 2.25%~5.11%. Moreover, the edge information of landslide image is smoother and the multi-scale landslide segmentation accuracy is better than other deep learning models, which demonstrates the effectiveness of the proposed model in semantic segmentation of landslides from remote sensing images. © 2023 Journal of Geo-Information Science. All rights reserved.