文献类型： 外文期刊

作者： Zhang, Xixin ¹ ; Yang, Yuhang ¹ ; Li, Zhiyong ¹ ; Ning, Xin ³ ; Qin, Yilang ⁴ ; Cai, Weiwei ⁵ ;

作者机构： 1.Sichuan Agr Univ, Coll Informat Engn, Yaan 625000, Sichuan, Peoples R China

2.Sichuan Key Lab Agr Informat Engn, Yaan 625000, Sichuan, Peoples R China

3.Chinese Acad Sci, Inst Semicond, Beijing 100083, Peoples R China

4.Henan Acad Agr Sci, Inst Agr Econ & Informat, Zhengzhou 450002, Henan, Peoples R China

5.Cent South Univ Forestry & Technol, Coll Logist & Transportat, Changsha 410004, Peoples R China

关键词： semantic segmentation; farmland vacancy segmentation; strip pooling; crop growth assessment; encoder– decoder

期刊名称：ENTROPY （ 影响因子：2.494； 五年影响因子：2.53 ）

ISSN：

年卷期： 2021 年 23 卷 4 期

页码：

收录情况： SCI

摘要： In the research of green vegetation coverage in the field of remote sensing image segmentation, crop planting area is often obtained by semantic segmentation of images taken from high altitude. This method can be used to obtain the rate of cultivated land in a region (such as a country), but it does not reflect the real situation of a particular farmland. Therefore, this paper takes low-altitude images of farmland to build a dataset. After comparing several mainstream semantic segmentation algorithms, a new method that is more suitable for farmland vacancy segmentation is proposed. Additionally, the Strip Pooling module (SPM) and the Mixed Pooling module (MPM), with strip pooling as their core, are designed and fused into the semantic segmentation network structure to better extract the vacancy features. Considering the high cost of manual data annotation, this paper uses an improved ResNet network as the backbone of signal transmission, and meanwhile uses data augmentation to improve the performance and robustness of the model. As a result, the accuracy of the proposed method in the test set is 95.6%, mIoU is 77.6%, and the error rate is 7%. Compared to the existing model, the mIoU value is improved by nearly 4%, reaching the level of practical application.