Interactive method for extracting boundaries of farmland of a grower based on satellite remote sensing and artificial intelligence

Abstract

The application discloses a planting household cultivated land boundary extraction interaction method based on satellite remote sensing and artificial intelligence, and belongs to the technical field of intelligent agriculture. The method comprises the following steps: a basic data preparation step, receiving and preprocessing satellite remote sensing image data, digital elevation model data and soil type data; a core interaction step, displaying the image on a front-end interface and receiving positive or negative feedback click operations of a user; a core algorithm processing step, inputting the interaction operation and multi-modal data into an agricultural segmentation arbitrary model optimized by agricultural field data, generating a candidate cultivated land boundary through multi-modal feature fusion, and outputting an optimal cultivated land boundary based on an area consistency loss function and a result selector; and a real-time feedback and iterative optimization step, feeding back the boundary result to the front end in real time. The application converts professional office work into simple field operation, and greatly reduces operation cost and technical threshold while ensuring result precision through a special model and a closed-loop interaction mechanism.

Claims

1. An interactive method for extracting farmland boundaries based on satellite remote sensing and artificial intelligence, characterized in that, Includes the following steps: Basic data preparation steps: Receive satellite remote sensing image data, digital elevation model data, and soil type data; perform orthorectification, image fusion, and atmospheric correction preprocessing on the satellite remote sensing image data. Core interactive steps: The preprocessed satellite remote sensing image is displayed on the front-end visual operation interface, and the user's interactive click operation on the satellite remote sensing image is received. The interactive click operation includes positive feedback clicks and negative feedback clicks. The core algorithm processing steps are as follows: The interactive click operation, satellite remote sensing image data, digital elevation model data, and soil type data are input into the arbitrary agricultural segmentation model. Candidate farmland boundaries are generated through multimodal feature fusion. The optimal farmland boundary is output based on the area consistency loss function and the result selector. Real-time feedback step: The optimal farmland boundary is fed back to the front-end visual operation interface for display in real time; Iterative optimization steps: Repeat the core interaction steps and core algorithm processing steps until the optimal farmland boundary matches the actual farmland boundary identified by the farmer; The arbitrary agricultural segmentation model processes data through the following steps: Multimodal feature fusion steps: Weighted fusion of satellite remote sensing image data, digital elevation model data, and soil type data to generate a fused feature map; Mask decoding steps: Input the fused feature map and the prompt encoding of the user interaction click into the mask decoder to generate candidate segmentation results; Result selection steps: Based on the confidence level of the candidate segmentation results and their consistency with the cultivated land area provided by the growers, select the optimal cultivated land boundary.

2. The interactive method for extracting farmland boundaries based on satellite remote sensing and artificial intelligence according to claim 1, characterized in that, In the core interaction steps: The positive feedback click is executed outside the existing farmland boundary range and is used to indicate the expansion of the farmland boundary range; The negative feedback click is executed in areas that are not actually cultivated land within the existing cultivated land boundary range, and is used to indicate the reduction of the cultivated land boundary range.

3. The interactive method for extracting farmland boundaries based on satellite remote sensing and artificial intelligence according to claim 1, characterized in that, The fusion function used in the multimodal feature fusion step is: ； Among them, the output It is a comprehensive feature map that integrates multi-dimensional information such as spectrum, topography, and phenology. It will replace single image features and be input into the subsequent mask decoder to provide richer and more robust contextual information for segmentation. , , These are image, elevation, and soil features, respectively. As learnable weights, their parameter values ​​will be automatically learned and adjusted during the training process through optimization algorithms; To activate the function, a nonlinear transformation is introduced, enabling the model to learn and represent more complex feature relationships, rather than a simple linear weighting. This is a bias term used to adjust the baseline of the fusion results, making the model fit better.

4. The interactive method for extracting farmland boundaries based on satellite remote sensing and artificial intelligence according to claim 1, characterized in that, The training process of the arbitrary agricultural segmentation model employs a total loss function that includes an area consistency loss function, which is: ； in, To predict the boundary area of ​​arable land, This represents the actual boundary area of ​​arable land. The total loss function is: ； in, is the total training loss, which is the objective that the model ultimately needs to minimize; is the intersection-union loss; directly optimizes the overlap area between the predicted boundary and the true boundary, guiding the model to learn how to generate accurate shapes and boundaries; This is due to the loss of area uniformity; The purpose of regularization loss is to prevent the model from overfitting, that is, the model performs too well on the training data and loses its ability to generalize to new data. , These are the weight hyperparameters for the area loss term and the regularization term, respectively, used to adjust their importance in the total loss.

5. The interactive method for extracting farmland boundaries based on satellite remote sensing and artificial intelligence according to claim 1, characterized in that, It also includes incremental learning steps: Collect data on farmland boundaries that users confirm are correct during the interaction process as new training samples; The parameters of the arbitrary agricultural segmentation model are updated using an elastic weight fixation strategy, and the loss function used is: ； in, The total loss is the overall objective function that the model needs to minimize during the incremental learning process. The loss is the standard loss on new tasks, which drives the model to learn new knowledge from new data; This is a real number greater than 0, set manually, to control the proportion of "retaining old knowledge" in the total loss. The larger the value, the stronger the model protects old knowledge, and the slower and more cautious it is in learning new knowledge. This is the Fisher information matrix, used to protect important parameters; The larger the value, the more important parameter i is in the old task, and the more it needs to be protected when learning new tasks to avoid major changes. The current parameter values ​​of the model; This represents the optimal value after training on the old task.

6. The interactive method for extracting farmland boundaries based on satellite remote sensing and artificial intelligence according to claim 1, characterized in that, The basic data preparation steps also include a step of data collection by growers themselves: Receive grower login information via mobile terminal; The mobile terminal receives on-site photos and location data taken by farmers on the cultivated land plots. The on-site photos and location data are associated with and stored with the grower's information.

7. The interactive method for extracting farmland boundaries based on satellite remote sensing and artificial intelligence according to claim 1, characterized in that, The result selector selects the optimal farmland boundary based on the following criteria: Calculate the confidence score for each candidate farmland boundary; Calculate the difference between the boundary area of ​​each candidate arable land and the reference value of arable land area provided by the grower; The candidate farmland boundary with the highest confidence score and the smallest area difference is selected as the optimal farmland boundary.

8. The interactive method for extracting farmland boundaries based on satellite remote sensing and artificial intelligence according to claim 1, characterized in that, Following the iterative optimization step, a manual correction step is also included: Manual vector correction is performed on local areas in the optimal cultivated land boundary where there is edge overlap, non-collinear edges of patches, or uneven boundaries.

9. The interactive method for extracting farmland boundaries based on satellite remote sensing and artificial intelligence according to claim 1, characterized in that, In the core interaction steps, when there is no location data collected by the growers themselves, the grower's plot of land is located using the following method: By combining visual judgment of map annotations in satellite imagery, search results for place names, and on-site identification by farmers, the spatial location of the farmers' plots can be determined.

Images