Abstract:The quantity and quality of cultivated land is the key to maintain the sustainable development of agriculture. Currently these information are obtained manually, which not only costs a lot of manpower and financial resources, but also has low efficiency and accuracy. Thus, it is much valuable to use satellite remote sensing images to identify and extract cultivated land. In this paper, an image segmentation network named SP-VNET is proposed, which contains Strip Pooling module and V-type segmentation model of void convolution while the transfer learning and image morphology processing are also applied. In this way, the cultivated land of satellite remote sensing image can be recognized and segmented more accurately. Compared with the six major semantic segmentation networks, the proposed SP-Vnet obtains the higher segmentation accuracy of OA, F1, and mean crossover ratio (mIoU) on the Mathorcup remote sensing image competition dataset for block recognition and segmentation. Experimental results show that SP-Vnet can enhance the global ability of the network to extract features, improve the segmentation ability, and significantly improve the accuracy of the cultivated land recognition. By combining with the post-processing operation of the traditional image processing method, our network obtains the smoother and the more recognition accuracy of the cultivated land edges.