Remote sensing image segmentation of gully erosion in a typical black soil area in Northeast China based on improved DeepLabV3+ model

Abstract

Gully erosion, a cause of soil degradation and reduced crop yields is widespread in the diffuse river and hillock regions of Northeast China. The surface conditions in gully erosion areas are complex, and manual extraction methods are inefficient, hindering gully erosion monitoring progress. Accurate extraction of gullies is essential for effective environmental monitoring and management. In this study, we propose an improved deep learning network, named DD-DA (the DeepLabV3+ deep network with dual attention mechanism), for automatic and precise gully erosion extraction from 2 m resolution Gaofen-6 remote sensing images from the Heshan Farm area. The DD-DA model builds upon the DeepLabV3+ network architecture with targeted optimizations. Firstly, ResNet50 replaces the original backbone network of DeepLabV3+, enhancing feature extraction capabilities. Second, the Convolutional Block Attention Module (CBAM) integrates spatial and channel attention, improving the model's capacity to capture the shape and texture features of gully erosion. Finally, the Dice loss function is adopted to address the limitations of the traditional cross-entropy loss function, particularly in handling unbalanced datasets. To promote deep learning applications in gully erosion monitoring in Northeast China, a gully erosion dataset was constructed. Experiments on gully erosion datasets show that DD-DA can achieve high Mean Intersection over Union (MIoU) (79.21 %), Pixel Accuracy (96.17 %), Recall (87.66 %), F1-Score (86.48 %), and Precision (86.08 %) simultaneously, which significantly outperform traditional image processing methods and recently deep learning networks. Overall, DD-DA efficiently and accurately segment gully erosion in agricultural fields, significantly improving the extraction of gully erosion features from high-resolution remote sensing images, particularly in the black soil regions of northeastern China. Meanwhile, this study offers a solution for the rapid monitoring of gully erosion and provides substantial technical support for extracting other land degradation features, such as soil salinization and desertification. • A gully erosion dataset in the black soil region of Northeast China was built. • We improved DeeplabV3+ to propose an DD-DA model for gully erosion segmentation. • Our proposed method achieves 79.21 % MIoU and 86.08 % Precision, respectively. • The DD-DA model provides a fast and accurate method for gully erosion extraction.