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The quantity and quality of cultivated land is the key to maintaining sustainable agricultural development. Satellite remote sensing images can be used to identify and obtain cultivated land areas. The accurate distribution of cultivated land can provide important support for national decision-making departments. In this paper, a deep learning image semantic segmentation model is established to complete the segmentation and extraction tasks of remote sensing plots. This article gives 8 data sets, including remote sensing pictures and corresponding real annotations. First of all, considering that the given data set has fewer samples, which will cause difficulty in model training, so different data enhancement methods are used. By rotating 90°/180°/270°, flipping, adjusting light, blurring, adding noise , the training data set was expanded to 3000. Considering that there is a certain positional relationship between cultivated land and background in spatial distribution, this paper innovatively proposes a CAttU-Net model that embeds spatial position information into the channel attention mechanism. Then, U-Net, AttU-Net and CAttU-Net models were established to solve the problem. In the specific training, in order to make the training process consistent with our goal, the loss function is designed as the weighted sum of CELoss and DiceLoss, and the Dice coefficient is used as the evaluation index to guide the model to perform better training, and finally the loss is converged to around 0.50. The model with the highest evaluation score on the validation set was used to predict and visualize part of the training set and validation set and the given test pictures, and the segmentation effects of different semantic segmentation models were intuitively discussed. Based on the semantic segmentation model evaluation index PA, MPA, MIoU, FWIoU, this paper quantitatively evaluates the key parameters in the recognition system, and the following conclusions are obtained: Based on the semantic segmentation evaluation index PA, compared with U-Net, AttU-Net improves By 8.14%, and CAttU-Net increases by 8.22%. In the evaluation index MIoU, AttU-Net increases by 15.58%, and CAttU-Net increases by 15.76%. It can be found that adding the attention mechanism can significantly improve the accuracy of remote sensing plot segmentation and extraction, and obtain a clearer segmentation boundary.
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