Agricultural Remote Sensing Image Cultivated Land Extraction Technology Based on Deep Learning

Land resources are the material basis for human survival and development. The characteristics of China’s land resources are “one more than three less”, that is, the total amount is large, the amount of cultivated land per capita is small, the quality of cultivated land is small, and the reserve resources that can be developed are few. The quantity and quality of cultivated land are related to China’s food security. Cultivated land protection has always been the core of land resource management. The acquisition of the quantity of cultivated land and its distribution information is the premise to achieve this goal. Remote sensing technology can objectively obtain the information of cultivated land from the spatial scale of wide area and local area. Remote sensing images have been widely used in large-scale land use surveys and remote sensing monitoring services. However, due to the details of their own ambiguity, large amount of data and large differences within the class, the difficulty of automatic interpretation of images has increased. This makes the actual business work still mainly based on manual visual interpretation, and lacks a highly automated and streamlined working style. Deep learning is a research field that has gradually emerged in recent years. At present, deep learning has become a mainstream research tool in the fields of speech recognition, image recognition, image classification, and target detection. Extracting the information of cultivated land from remote sensing images is a problem of image recognition and classification. Therefore, using deep learning to extract cultivated land from agricultural remote sensing images is a very feasible research program. Based on the deep learning theory, this paper sorts out the research results in the field of remote sensing image classification and deep learning at home and abroad, then preprocesses and labels the remote sensing image to make a training sample set, and finally uses convolutional neural network (CNN). For the classifier, the agricultural remote sensing image was extracted from cultivated land.