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Open AccessArticle

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

by 1,2,†, 3,4,*,†, 3,†, 5, 3, 6 and 6
1
School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China
2
Shandong Provincial Climate Center, NO.12 Wuying Mountain Road, Jinan 250001, China
3
College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, China
4
Shandong Technology and Engineering Center for Digital Agriculture, 61 Daizong Road, Taian 271000, China
5
School of Computer Science, Hubei University of Technology, 28 Nanli Road, Wuhan 430068, China
6
College of Ocean and Space Information, China University of Petroleum (East China), Qingdao 266580, China
*
Author to whom correspondence should be addressed.
These authors are co-first authors as they contributed equally to this work.
Remote Sens. 2020, 12(3), 538; https://doi.org/10.3390/rs12030538
Received: 23 December 2019 / Revised: 29 January 2020 / Accepted: 4 February 2020 / Published: 6 February 2020
Improving the accuracy of edge pixel classification is an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method using prior knowledge obtained from statistical analysis to refine CNN classification results, named post-processing CNN (PP-CNN). First, we used an improved RefineNet model to roughly segment remote sensing imagery in order to obtain the initial winter wheat area and the category probability vector for each pixel. Second, we used manual labels as references and performed statistical analysis on the class probability vectors to determine the filtering conditions and select the pixels that required optimization. Third, based on the prior knowledge that winter wheat pixels were internally similar in color, texture, and other aspects, but different from other neighboring land-use types, the filtered pixels were post-processed to improve the classification accuracy. We used 63 Gaofen-2 images obtained from 2017 to 2019 of a representative Chinese winter wheat region (Feicheng, Shandong Province) to create the dataset and employed RefineNet and SegNet as standard CNN and conditional random field (CRF) as post-process methods, respectively, to conduct comparison experiments. PP-CNN’s accuracy (94.4%), precision (93.9%), and recall (94.4%) were clearly superior, demonstrating its advantages for the improved refinement of edge areas during image classification. View Full-Text
Keywords: convolutional neural network; semantic features; statistical features; Gaofen-2 imagery; winter wheat; post-processing; spatial distribution; Feicheng; China convolutional neural network; semantic features; statistical features; Gaofen-2 imagery; winter wheat; post-processing; spatial distribution; Feicheng; China
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MDPI and ACS Style

Li, F.; Zhang, C.; Zhang, W.; Xu, Z.; Wang, S.; Sun, G.; Wang, Z. Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis. Remote Sens. 2020, 12, 538. https://doi.org/10.3390/rs12030538

AMA Style

Li F, Zhang C, Zhang W, Xu Z, Wang S, Sun G, Wang Z. Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis. Remote Sensing. 2020; 12(3):538. https://doi.org/10.3390/rs12030538

Chicago/Turabian Style

Li, Feng; Zhang, Chengming; Zhang, Wenwen; Xu, Zhigang; Wang, Shouyi; Sun, Genyun; Wang, Zhenjie. 2020. "Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis" Remote Sens. 12, no. 3: 538. https://doi.org/10.3390/rs12030538

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