Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour, Adel

doi:10.1109/access.2021.3086269

Cited by 20 publications

(6 citation statements)

References 70 publications

(70 reference statements)

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“…The results of this study confirm that FS methods play a crucial role in distinguishing crop types in different machine learning models, which is consistent with previous findings [42,43]. A robust FS method should be able to rank and reduce a large number of input features [44]. In this study, we used the OF-RF-RFE method to select features and successfully reduced the initial sixty-five features to eight.…”

Section: Discussion and Future Worksupporting

confidence: 90%

Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery

Cao,

Dai,

Zhang

et al. 2024

Agriculture

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This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monitoring of food crops and precision agriculture management. “Staple-food-shifting” refers to the planting of other cash crops on cultivated land that should have been planted with staple crops such as wheat, rice, and maize, resulting in a change in the type of arable land cultivated. An accurate grasp of the spatial and temporal patterns of “staple-food-shifting” on arable land is an important basis for rationalizing land use and protecting food security. In this study, the Shihezi Reclamation Area in Xinjiang is selected as the study area, and Gaofen-6 satellite images are used to study the changes in the cultivated area of staple food crops and their regional distribution. Firstly, the images are segmented at multiple scales and four types of features are extracted, totaling sixty-five feature variables. Secondly, six feature selection algorithms are used to optimize the feature variables, and a total of nine feature combinations are designed. Finally, k-Nearest Neighbor (KNN), Random Forest (RF), and Decision Tree (DT) are used as the basic models of image classification to explore the best combination of feature selection method and machine learning model suitable for wheat, maize, and cotton classification. The results show that our proposed optimal feature selection method (OFSM) can significantly improve the classification accuracy by up to 15.02% compared to the Random Forest Feature Importance Selection (RF-FI), Random Forest Recursive Feature Elimination (RF-RFE), and XGBoost Feature Importance Selection (XGBoost-FI) methods. Among them, the OF-RF-RFE model constructed based on KNN performs the best, with the overall accuracy, average user accuracy, average producer accuracy, and kappa coefficient reaching 90.68%, 87.86%, 86.68%, and 0.84, respectively.

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Section: Discussion and Future Worksupporting

confidence: 90%

Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery

Cao,

Dai,

Zhang

et al. 2024

Agriculture

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“…It is one of the more practical algorithms in scientific research. Currently, it specializes in tasks such as face recognition, digital writing, text distribution, and data retrieval [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

“…Linear indivisibility means that the categories cannot be completely separated by an optimal classification plane. At this time, the limiting conditions in formula (7) can be appropriately relaxed and a relaxation factor ξ i can be introduced, and formula (3) becomes as shown in formula (11):…”

Section: Two Kinds Of Linear Nonseparable Svmmentioning

confidence: 99%

Attribute Feature Classification of English Grammar Entry Base Based on Support Vector Machine Classification Algorithm

Yinghua

Shaoxiu

Juan

2022

Computational Intelligence and Neuroscience

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An attribute feature classification method of English grammar vocabulary entry database based on support vector machine classification algorithm is proposed; this method takes news English as the research object and focuses on the classification of attributes and features of the English grammar lexicon database. First, the k-means algorithm is used to cluster the training set, and the one-to-many method is used to train two types of classifiers for the texts that cannot be correctly clustered in each class, that is, the classifiers of the corresponding categories are trained, and then the training set passed through a pair of the classifier generated by multiple SVMs is tested, and the samples that fall in the inseparable area are retrained by a one-to-one method, so as to achieve the purpose of balancing the training samples and reducing the inseparable area. The results show that, compared with the FDAGSVM algorithm, the proposed three multiclass classification algorithms have significantly improved classification speed and classification accuracy, and the macro average accuracy rates are 77.94%, 73.94%, and 72.36%, respectively. While ensuring the classification speed and classification accuracy of the single-label samples, the multiclass classification is realized, and it has high accuracy, recall rate, and value, which better solves the multiclass classification problem and expands the classification capability of the support vector machine. In addition, a comprehensive index based on the SVM classification algorithm is proposed to ensure the specialization of the attribute feature classification.

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“…If these noises are not filtered, the subsequent description and expression of fracture information cannot be carried out. According to the morphological difference between the target information area and the noise area, it is an important denoising method to filter irrelevant noise by using connected domain labeling [6][7].…”

Section: Connected Domain Analysismentioning

confidence: 99%

Highway Crack Material Detection Algorithm Based on Digital Image Processing Technology

Zhang

2023

J. Phys.: Conf. Ser.

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After entering the 21st century, China’s economic development is changing with each passing day, and the total economic volume has ranked second in the world. After the quantitative problem is solved, the qualitative problem becomes more and more important. In order to make the expressway be used healthily and for a long time, simple and efficient expressway maintenance methods become extremely important. In this paper, based on the study of the predecessors’ related research on road crack identification, based on digital image processing technology, the material detection algorithm for expressway cracks is studied. The advantages and disadvantages of the existing algorithms are analyzed, and the main types of cracks in the expressway are analyzed. The causes and characteristics of each type of cracks are understood, and the problems of current road crack detection and identification algorithms are analyzed. Through the experimental test analysis of the crack material detection algorithm, various types of crack detection images are selected from the horizontal and vertical crack detection results to form a test sample image library. The validity of the classification algorithm is tested, and the test results verify the validity of the algorithm in this paper.

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Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Cited by 20 publications

References 70 publications

Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery

Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery

Attribute Feature Classification of English Grammar Entry Base Based on Support Vector Machine Classification Algorithm

Highway Crack Material Detection Algorithm Based on Digital Image Processing Technology

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