An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field
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College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, Sichuan, China
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Sichuan Key Laboratory of Agricultural Information Engineering, Ya’an 625000, Sichuan, China
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Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
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Institute of Agricultural Economy and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, Henan, China
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College of Logistics and Transportation, Central South University of Forestry and Technology, Changsha 410004, Hunan, China
*
Author to whom correspondence should be addressed.
Academic Editor: Michal Choras
Entropy 2021, 23(4), 435; https://doi.org/10.3390/e23040435
Received: 11 March 2021 / Revised: 1 April 2021 / Accepted: 1 April 2021 / Published: 8 April 2021
(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)
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.
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Keywords:
semantic segmentation; farmland vacancy segmentation; strip pooling; crop growth assessment; encoder–decoder
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MDPI and ACS Style
Zhang, X.; Yang, Y.; Li, Z.; Ning, X.; Qin, Y.; Cai, W. An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field. Entropy 2021, 23, 435. https://doi.org/10.3390/e23040435
AMA Style
Zhang X, Yang Y, Li Z, Ning X, Qin Y, Cai W. An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field. Entropy. 2021; 23(4):435. https://doi.org/10.3390/e23040435
Chicago/Turabian StyleZhang, Xixin; Yang, Yuhang; Li, Zhiyong; Ning, Xin; Qin, Yilang; Cai, Weiwei. 2021. "An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field" Entropy 23, no. 4: 435. https://doi.org/10.3390/e23040435
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