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Article

Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method

by 1,2, 1,2,*, 1,2 and 1
1
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
2
Key Laboratory of Agricultural Land Quality, Ministry of Land and Resources of the China, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Received: 19 February 2019 / Revised: 2 April 2019 / Accepted: 3 April 2019 / Published: 11 April 2019
The growing population in China has led to an increasing importance of crop area (CA) protection. A powerful tool for acquiring accurate and up-to-date CA maps is automatic mapping using information extracted from high spatial resolution remote sensing (RS) images. RS image information extraction includes feature classification, which is a long-standing research issue in the RS community. Emerging deep learning techniques, such as the deep semantic segmentation network technique, are effective methods to automatically discover relevant contextual features and get better image classification results. In this study, we exploited deep semantic segmentation networks to classify and extract CA from high-resolution RS images. WorldView-2 (WV-2) images with only Red-Green-Blue (RGB) bands were used to confirm the effectiveness of the proposed semantic classification framework for information extraction and the CA mapping task. Specifically, we used the deep learning framework TensorFlow to construct a platform for sampling, training, testing, and classifying to extract and map CA on the basis of DeepLabv3+. By leveraging per-pixel and random sample point accuracy evaluation methods, we conclude that the proposed approach can efficiently obtain acceptable accuracy (Overall Accuracy = 95%, Kappa = 0.90) of CA classification in the study area, and the approach performs better than other deep semantic segmentation networks (U-Net/PspNet/SegNet/DeepLabv2) and traditional machine learning methods, such as Maximum Likelihood (ML), Support Vector Machine (SVM), and RF (Random Forest). Furthermore, the proposed approach is highly scalable for the variety of crop types in a crop area. Overall, the proposed approach can train a precise and effective model that is capable of adequately describing the small, irregular fields of smallholder agriculture and handling the great level of details in RGB high spatial resolution images. View Full-Text
Keywords: agriculture; high spatial resolution images; semantic labeling agriculture; high spatial resolution images; semantic labeling
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MDPI and ACS Style

Du, Z.; Yang, J.; Ou, C.; Zhang, T. Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method. Remote Sens. 2019, 11, 888. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070888

AMA Style

Du Z, Yang J, Ou C, Zhang T. Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method. Remote Sensing. 2019; 11(7):888. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070888

Chicago/Turabian Style

Du, Zhenrong, Jianyu Yang, Cong Ou, and Tingting Zhang. 2019. "Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method" Remote Sensing 11, no. 7: 888. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070888

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