Research On Land Extraction And Crop Recognition Method Based On Deep Neural Network

China is a big agricultural country,but natural disasters are inevitable due to geographical environment,climate and other factors.Based on risk transfer mechanism-agricultural insurance guarantees post-disaster reconstruction and recovery of agricultural production,which is one of the important ways to support agricultural development.With the development of remote sensing technology,more and more remote sensing images are used to carry out agricultural insurance business,especially under-writing business.The core of underwriting business is to accurately realize the extraction of cultivated land and crop identification at the plot level.In this thesis,the problems of these two parts are studied respectively.In terms of cultivated land extraction,the current main method is to model cultivated land extraction as a bottom-up edge extraction task,which requires a series of post-processing algorithms such as edge closure and region segmentation to obtain plot objects.This method is not only low in processing efficiency,but also prone to accumulative errors.In this thesis,the extraction of cultivated land is modeled as a top-down instance segmentation task,so as to extract the land object from end to end.In order to improve the accuracy of the single-stage contour-based instance segmentation model,this thesis introduces the idea of polar coordinate modeling into the two-stage instance segmentation model,the polar coordinate branch is added,and the instance contour and mask are supervised at the same time.Finally,an instance of the plot is obtained through a limited number of contour points.In terms of crop recognition,the crop features of remote sensing images in different periods are different,which leads to changes in the distribution of data features and seriously affects the recognition accuracy of the supervision model.In this thesis,a domain adaptive model is used to deal with this problem and realize cross-temporal crop recognition.Based on CDAN model,a crop recognition method based on the learnable sample weight CDAN model is proposed.On the one hand,according to the characteristics of remote sensing images and crops,the model provides rich features for downstream migration tasks by widening the backbone network and using multi-time images;at the same time,in order to solve the problem of difficult transfer caused by hard samples in training,a learnable sample weighted network is used instead of calculating entropy directly to measure the transferability of samples better.In order to verify the validity of the cultivated land extraction and crop identification model proposed in this thesis,the thesis uses Yizheng City,Jiangsu Province as the experimental area,and uses google satellite images and multi-phase Sentinel-2 satellite images as data sources to conduct on-site sampling and manual labeling.Experiments show that the introduction of the contour polar coordinate branch in the two-stage instance segmentation model can effectively improve the accuracy of the contour modeling-based model,and the AP index of the plot extraction reaches 61.5%;Using the improved CDAN-SWN model proposed in this thesis,the metrics in a variety of rice identification data transfer scenarios have been greatly improved,and the final identification accuracy can reach 97%.