Author(s): Zhang, XX (Zhang, Xixin); Yang, YH (Yang, Yuhang); Li, ZY (Li, Zhiyong); Ning, X (Ning, Xin); Qin, YL (Qin, Yilang); Cai, WW (Cai, Weiwei)

Source: ENTROPY Volume: 23 Issue: 4 Article Number: 435 DOI: 10.3390/e23040435 Published: APR 2021

Abstract: 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.

Accession Number: WOS:000642950300001

PubMed ID: 33917753

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Ning, Xin         M-9479-2018         0000-0001-7897-1673

Cai, Weiwei         AAH-5456-2020         0000-0001-6795-6152

eISSN: 1099-4300

Full Text: https://www.mdpi.com/1099-4300/23/4/435