2.3 Selection of ephemeral gully identification model
The SegNet model is a convolutional neural network for pixel-level image
segmentation proposed by Badrinarayanan et al. (2017) using
end-to-end and pixel-to-pixel training to achieve image segmentation.
Its core component is an encoder network and corresponding decoder
network, followed by a pixel-level classifier, which outputs the
probability map of the K channel, where K is the number of
classification categories. Compared with other models, SegNet has fewer
training parameters, smaller memory occupation, and shorter network
training time (Deng et al., 2022; Jiang et al., 2020; Manickam et al.,
2020). In 2021, six indexes (Accuracy, Precision, Recall, F1 value, ROC
curve, and AUC) were used to compare the ephemeral gully recognition
results and accuracy evaluation with U-Net, R2U-Net, and SegNet, with
SegNet ranked first for ephemeral gully recognition in the hilly and
gully region of the Loess Plateau, followed by R2U-Net and U-Net (Liuet al. , 2022). Ephemeral gully length and width between predicted
and field measured values had RMSE values of 6.78 m
(R2 = 0.9817) and 0.50 m (R2 =
0.8573), respectively, using the SegNet model, indicating its superior
performance for ephemeral gully recognition and morphological feature
extraction. Hence, this study used the SegNet model for ephemeral gully
recognition and morphological feature extraction at the watershed scale.
The process of identifying ephemeral gullies with the SegNet model was
performed as follows: SegNet model carries out iterative cyclic training
based on the data set until the loss value is minimum and obtains the
corresponding optimal weight of the model. Based on the optimal weight
and network structure of the SegNet model, ephemeral gullies were
recognized using the sliding window and ignoring edge detection methods
at the watershed scale (Fig. 3).