scholarly journals Erratum: Wang, S., et al. Improved Winter Wheat Spatial Distribution Extraction Using a Convolutional Neural Network and Partly Connected Conditional Random Field. Remote Sensing 2020, 12, 821

2020 ◽  
Vol 12 (10) ◽  
pp. 1568
Author(s):  
Shouyi Wang ◽  
Zhigang Xu ◽  
Chengming Zhang ◽  
Yuanyuan Wang ◽  
Shuai Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Winter Wheat ◽  
Convolutional Neural Network ◽  
Random Field ◽  
Conditional Random Field

After re-considering the contribution of Jinghan Zhang, Zhongshan Mu, and Tianyu Zhao, respectively, we wish to remove them from the authorship of our paper [...]

scholarly journals Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field

2020 ◽  
Vol 12 (5) ◽  
pp. 821 ◽  
Author(s):  
Shouyi Wang ◽  
Zhigang Xu ◽  
Chengming Zhang ◽  
Jinghan Zhang ◽  
Zhongshan Mu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Winter Wheat ◽  
Random Field ◽  
Large Scale ◽  
Conditional Random Field ◽  
Pixel Classification ◽  
Classification Result ◽  
Edge Pixel ◽  
Remote Sensing Imagery

Improving the accuracy of edge pixel classification is crucial for extracting the winter wheat spatial distribution from remote sensing imagery using convolutional neural networks (CNNs). In this study, we proposed an approach using a partly connected conditional random field model (PCCRF) to refine the classification results of RefineNet, named RefineNet-PCCRF. First, we used an improved RefineNet model to initially segment remote sensing images, followed by obtaining the category probability vectors for each pixel and initial pixel-by-pixel classification result. Second, using manual labels as references, we performed a statistical analysis on the results to select pixels that required optimization. Third, based on prior knowledge, we redefined the pairwise potential energy, used a linear model to connect different levels of potential energies, and used only pixel pairs associated with the selected pixels to build the PCCRF. The trained PCCRF was then used to refine the initial pixel-by-pixel classification result. We used 37 Gaofen-2 images obtained from 2018 to 2019 of a representative Chinese winter wheat region (Tai’an City, China) to create the dataset, employed SegNet and RefineNet as the standard CNNs, and a fully connected conditional random field as the refinement methods to conduct comparison experiments. The RefineNet-PCCRF’s accuracy (94.51%), precision (92.39%), recall (90.98%), and F1-Score (91.68%) were clearly superior than the methods used for comparison. The results also show that the RefineNet-PCCRF improved the accuracy of large-scale winter wheat extraction results using remote sensing imagery.

scholarly journals Convolutional Neural Network-Based Remote Sensing Images Segmentation Method for Extracting Winter Wheat Spatial Distribution

2018 ◽  
Vol 8 (10) ◽  
pp. 1981 ◽  
Author(s):  
Chengming Zhang ◽  
Shuai Gao ◽  
Xiaoxia Yang ◽  
Feng Li ◽  
Maorui Yue ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Winter Wheat ◽  
Convolutional Neural Network ◽  
Hybrid Structure ◽  
Remote Sensing Images ◽  
Accurate Identification ◽  
Edge Pixel ◽  
Wheat Field

When extracting winter wheat spatial distribution by using convolutional neural network (CNN) from Gaofen-2 (GF-2) remote sensing images, accurate identification of edge pixel is the key to improving the result accuracy. In this paper, an approach for extracting accurate winter wheat spatial distribution based on CNN is proposed. A hybrid structure convolutional neural network (HSCNN) was first constructed, which consists of two independent sub-networks of different depths. The deeper sub-network was used to extract the pixels present in the interior of the winter wheat field, whereas the shallower sub-network extracts the pixels at the edge of the field. The model was trained by classification-based learning and used in image segmentation for obtaining the distribution of winter wheat. Experiments were performed on 39 GF-2 images of Shandong province captured during 2017–2018, with SegNet and DeepLab as comparison models. As shown by the results, the average accuracy of SegNet, DeepLab, and HSCNN was 0.765, 0.853, and 0.912, respectively. HSCNN was equally as accurate as DeepLab and superior to SegNet for identifying interior pixels, and its identification of the edge pixels was significantly better than the two comparison models, which showed the superiority of HSCNN in the identification of winter wheat spatial distribution.

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

2020 ◽  
Vol 12 (3) ◽  
pp. 538 ◽  
Author(s):  
Feng Li ◽  
Chengming Zhang ◽  
Wenwen Zhang ◽  
Zhigang Xu ◽  
Shouyi Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Statistical Analysis ◽  
Winter Wheat ◽  
Prior Knowledge ◽  
Conditional Random Field ◽  
Semantic Features ◽  
Edge Pixel ◽  
Remote Sensing Imagery ◽  
Post Process

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.

scholarly journals Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network

2019 ◽  
Vol 9 (14) ◽  
pp. 2917 ◽  
Author(s):  
Yan Chen ◽  
Chengming Zhang ◽  
Shouyi Wang ◽  
Jianping Li ◽  
Feng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Convolutional Neural Network ◽  
Spectral Feature ◽  
Input Image ◽  
Remote Sensing Images ◽  
Feature Map ◽  
Feature Extractor ◽  
Extraction Unit

Using satellite remote sensing has become a mainstream approach for extracting crop spatial distribution. Making edges finer is a challenge, while simultaneously extracting crop spatial distribution information from high-resolution remote sensing images using a convolutional neural network (CNN). Based on the characteristics of the crop area in the Gaofen 2 (GF-2) images, this paper proposes an improved CNN to extract fine crop areas. The CNN comprises a feature extractor and a classifier. The feature extractor employs a spectral feature extraction unit to generate spectral features, and five coding-decoding-pair units to generate five level features. A linear model is used to fuse features of different levels, and the fusion results are up-sampled to obtain a feature map consistent with the structure of the input image. This feature map is used by the classifier to perform pixel-by-pixel classification. In this study, the SegNet and RefineNet models and 21 GF-2 images of Feicheng County, Shandong Province, China, were chosen for comparison experiment. Our approach had an accuracy of 93.26%, which is higher than those of the existing SegNet (78.12%) and RefineNet (86.54%) models. This demonstrates the superiority of the proposed method in extracting crop spatial distribution information from GF-2 remote sensing images.

scholarly journals Finger Vein Verification Algorithm Based on Fully Convolutional Neural Network and Conditional Random Field

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 65402-65419 ◽  
Author(s):  
Junying Zeng ◽  
Fan Wang ◽  
Jianxiang Deng ◽  
Chuanbo Qin ◽  
Yikui Zhai ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Random Field ◽  
Conditional Random Field ◽  
Verification Algorithm ◽  
Finger Vein
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