Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints

Liu, Yuxuan; Aleksandrov, Mitko; Zlatanova, Sisi; Zhang, Junjun; Mo, Fan; Chen, Xiaojian

doi:10.3390/s19214717

Cited by 16 publications

(8 citation statements)

References 17 publications

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“…To verify the effectiveness of the method in this paper, RF [ 4 ], AdaBoost [ 16 ] and SVM [ 26 ] algorithms are used to classify the point cloud of the transmission corridor. The comparison table of classification results is shown in Table 5 .…”

Section: Resultsmentioning

confidence: 99%

“…Many classification algorithms have been proposed. These algorithms can be divided into two classes according to the methods of feature extraction: (1) feature extraction by handcrafting [ 14 , 15 ]; (2) feature extraction by machine learning [ 16 , 17 , 18 , 19 ]. The methods of the first class establish a feature database by manually extracting feature parameters and the classification is conducted by matching the given features with the feature database.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Classification Method of Point Clouds of Transmission Line Corridor Based on Improved Random Forest and Multi-Scale Features

Tang

Zhang

Lan

et al. 2023

Sensors

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Classification of airborne laser scanning (ALS) point clouds of power lines is of great importance to their reconstruction. However, it is still a difficult task to efficiently and accurately classify the ground, vegetation, power lines and power pylons from ALS point clouds. Therefore, in this paper, a method is proposed to improve the accuracy and efficiency of the classification of point clouds of transmission lines, which is based on improved Random Forest and multi-scale features. The point clouds are filtered by the optimized progressive TIN densification filtering algorithm, then the elevations of the filtered point cloud are normalized. The features of the point cloud at different scales are calculated according to the basic features of the point cloud and the characteristics of transmission lines. The Relief F and Sequential Backward Selection algorithm are used to select the best subset of features to estimate the parameters of the learning model, then an Improved Random Forest classification model is built to classify the point clouds. The proposed method is verified by using three different samples from the study area and the results show that, compared with the methods based on Support Vector Machines, AdaBoost or Random Forest, our method can reduce feature redundancy and has higher classification accuracy and efficiency.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Classification Method of Point Clouds of Transmission Line Corridor Based on Improved Random Forest and Multi-Scale Features

Tang

Zhang

Lan

et al. 2023

Sensors

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show abstract

“…A novel model for PPAI was developed to predict aptamers and protein-aptamer interactions with a machine learning framework integrated adaboost [33] and random forest [34]. Adaboost combines multiple weak classifiers into the final strong classifier.…”

Section: Ppai Model Based On Integrated Framework Of Adaboost and Ranmentioning

confidence: 99%

PPAI: a web server for predicting protein-aptamer interactions

et al. 2020

BMC Bioinformatics

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Background: The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies. Results: In this study, a novel web server named PPAI is developed to predict aptamers and protein-aptamer interactions with key sequence features of proteins/ aptamers and a machine learning framework integrated adaboost and random forest. A new method for extracting several key sequence features of both proteins and aptamers is presented, where the features for proteins are extracted from amino acid composition, pseudo-amino acid composition, grouped amino acid composition, C/T/D composition and sequence-order-coupling number, while the features for aptamers are extracted from nucleotide composition, pseudo-nucleotide composition (PseKNC) and normalized Moreau-Broto autocorrelation coefficient. On the basis of these feature sets and balanced the samples with SMOTE algorithm, we validate the performance of PPAI by the independent test set. The results demonstrate that the Area Under Curve (AUC) is 0.907 for prediction of aptamer, while the AUC reaches 0.871 for prediction of protein-aptamer interactions. Conclusion: These results indicate that PPAI can query aptamers and proteins, predict aptamers and predict protein-aptamer interactions in batch mode precisely and efficiently, which would be a novel bioinformatics tool for the research of protein-aptamer interactions. PPAI web-server is freely available at http://39.96.85.9/ PPAI.

show abstract

“…A novel model for PPAI was developed to predict aptamers and protein-aptamer interactions with a machine learning framework integrated adaboost [34] and random forest [35]. Adaboost combines multiple weak classifiers into the final strong classifier.…”

Section: Ppai Model Based On Integrated Framework Of Adaboost and Ranmentioning

confidence: 99%

PPAI: a web server for predicting protein-aptamer interactions

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies. Results: In this study, a novel web server named PPAI is developed to predict aptamers and protein-aptamer interactions with key sequence features of proteins/aptamers and a machine learning framework integrated adaboost and random forest. A new method for extracting several key sequence features of both proteins and aptamers is presented, where the features for proteins are extracted from amino acid composition, pseudo-amino acid composition, grouped amino acid composition, C/T/D composition and sequence-order-coupling number, while the features for aptamers are extracted from nucleotide composition, pseudo-nucleotide composition (PseKNC) and Moreau-Broto autocorrelation coefficient. On the basis of these feature sets and balanced the samples with SMOTE algorithm, we validate the performance of PPAI by the independent test set. The results demonstrate that the Area Under Curve (AUC) is 0.907 for prediction of aptamer, while the AUC reaches 0.871 for prediction of protein-aptamer interactions. Conclusion: These results indicate that PPAI can query aptamers, predict aptamers and predict protein-aptamer interactions in batch mode precisely and efficiently, which would be a novel bioinformatics tool for the research of protein-aptamer interactions. PPAI web-server is freely available at http://39.96.85.9/PPAI.

show abstract

Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints

Cited by 16 publications

References 17 publications

A Classification Method of Point Clouds of Transmission Line Corridor Based on Improved Random Forest and Multi-Scale Features

A Classification Method of Point Clouds of Transmission Line Corridor Based on Improved Random Forest and Multi-Scale Features

PPAI: a web server for predicting protein-aptamer interactions

PPAI: a web server for predicting protein-aptamer interactions

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