Polsar Image Crop Classification Based on Deep Residual Learning Network

Mei, Xin; Nie, Wen; Liu, Junyi; Huang, Kui

doi:10.1109/agro-geoinformatics.2018.8476061

Cited by 10 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e OA of Ref. [27] method is only 90.65%, and the Kappa index is 0.864. e OA of Ref. [26] method is 91.37%, and the Kappa index is 0.874. e OA of the proposed method is 92.14%, and the Kappa index is 0.896. ese two indexes are the best.…”

Section: Performance Comparison With Other Methodsmentioning

confidence: 95%

“…In order to prove the advantages of the proposed crop classification and recognition method based on improved U-Net, classification methods in Ref. [26,27] are compared with the proposed method under the same experimental conditions. e experimental results are shown in Table 3.…”

Section: Performance Comparison With Other Methodsmentioning

confidence: 99%

“…e two models of Ref. [26,27] cannot fully and effectively integrate the feature information of crop remote sensing images, so the classification effect on the result map is poor. erefore, experiments show that the proposed method is feasible and efficient for crop classification and recognition in UAV remote sensing images.…”

Section: Scientific Programmingmentioning

confidence: 99%

“…A crop classification method based on convolutional neural network (CNN) and Visual Geometry Group (VGG) is proposed to classify crops. Reference [27] selected the target decomposition method to calculate the multidimensional feature images of the target and constructed a PolSAR classification model based on ResNet. However, when the above methods classify and recognize the remote sensing images of crops, there are often some problems, such as incomplete extraction of remote sensing image classification features and low classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent Identification Method of Crop Species Using Improved U-Net Network in UAV Remote Sensing Image

Liu

2022

Scientific Programming

View full text Add to dashboard Cite

Aiming at the problems of incomplete classification features extraction of remote sensing images and low accuracy of crop classification in existing crop classification and recognition methods, a crop classification and recognition method using improved U-Net in Unmanned Aerial Vehicle (UAV) remote sensing images is proposed. First, the experimental data set is preprocessed. The data set is expanded by flipping transformation, translation transformation, and random cutting, which expands the number of data sets, and then the original image is cut to meet the requirements of the experiment for image size; Moreover, the network structure of U-Net is deepened, and the atrous spatial pyramid pooling (ASPP) structure is introduced after the encoder to better understand the semantic information and improve the ability of model mining data features; Finally, in order to prevent the overfitting of the deepened model, the dropout layer is introduced to weaken the joint adaptability between various neurons. Experiments show that the comprehensive OA and Kappa indexes of the proposed crop classification and recognition method based on improved U-Net are 92.14% and 0.896, respectively, which are better than the comparison methods. Therefore, the proposed method has good ability of crop classification and recognition.

show abstract

Section: Performance Comparison With Other Methodsmentioning

confidence: 95%

Section: Performance Comparison With Other Methodsmentioning

confidence: 99%

Section: Scientific Programmingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Identification Method of Crop Species Using Improved U-Net Network in UAV Remote Sensing Image

Liu

2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…With the integration of modern information technology such as artificial intelligence, big data, and the Internet of Things with agricultural development, smart agriculture has become the inevitable direction of agricultural development ( Kussul et al, 2017 ; Mei et al, 2018 ). As one of the important contents of the development of smart agriculture, the intelligent identification and classification of crop varieties is crucial to the management of the differences in later crop production ( Suh et al, 2018 ; Khamparia et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings

Zhang

2022

Front. Plant Sci.

View full text Add to dashboard Cite

Accurate identification of crop varieties is an important aspect of smart agriculture, which is not only essential for the management of later crop differences, but also has a significant effect on unmanned operations in planting scenarios such as facility greenhouses. In this study, five kinds of lettuce under the cultivation conditions of greenhouses were used as the research object, and a classification model of lettuce varieties with multiple growth stages was established. First of all, we used the-state-of-the-art method VOLO-D1 to establish a variety classification model for the 7 growth stages of the entire growth process. The results found that the performance of the lettuce variety classification model in the SP stage needs to be improved, but the classification effect of the model at other stages is close to 100%; Secondly, based on the challenges of the SP stage dataset, we combined the advantages of the target detection mechanism and the target classification mechanism, innovatively proposed a new method of variety identification for the SP stage, called YOLO-VOLO-LS. Finally, we used this method to model and analyze the classification of lettuce varieties in the SP stage. The result shows that the method can achieve excellent results of 95.961, 93.452, 96.059, 96.014, 96.039 in Val-acc, Test-acc, Recall, Precision, F1-score, respectively. Therefore, the method proposed in this study has a certain reference value for the accurate identification of varieties in the early growth stage of crops.

show abstract