Comparison of RFE-DL and stacking ensemble learning algorithms for classifying mangrove species on UAV multispectral images

Fu, Bolin; He, Xu; Yao, Hang; Liang, Yiyin; Deng, Tengfang; He, Hongchang; Fan, Donglin; Lan, Guiwen; He, Wen

doi:10.1016/j.jag.2022.102890

Cited by 29 publications

(34 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SEL framework generalizes the output values of multiple models to improve the overall classification performance by using the classification results of the base model as the input data of the meta-model ( Fu et al., 2022 ). The SEL principle was shown in Figure 3A .…”

Section: Methodsmentioning

confidence: 99%

“…(Zhang et al, 2020) classifies vegetation based on medium resolution spectral imaging technology and SEL, and its accuracy is 5.1-5.2% higher than other single models. (Fu et al, 2022) constructed a model based on multispectral images and SEL, and The training and test set accuracies of the feature wavelengths selected by the four variable selection methods as the model input. Wu et al 10.3389/fpls.2022.1047479 found that the integrated learning algorithm produced better classification performance than the basic model, with an overall accuracy rate of 1.6-12.7% higher.…”

Section: Classification Performance Analysis Of Stacked Ensemble Modelsmentioning

confidence: 99%

“…In theory, different base learners can give full play to the cooperative advantages of ensemble learning and achieve the effect of complementary integration ( He et al., 2022 ). Stacked ensemble machine learning algorithms have been successfully used in various applications including wind power prediction ( Dong et al., 2021 ), soil classification ( Eyo and Abbey, 2022 ), species classification ( Fu et al., 2022 ), etc. ( Zhang et al., 2020 ) classifies vegetation based on medium resolution spectral imaging technology and SEL, and its accuracy is 5.1-5.2% higher than other single models.…”

Section: Introductionmentioning

confidence: 99%

“…( Zhang et al., 2020 ) classifies vegetation based on medium resolution spectral imaging technology and SEL, and its accuracy is 5.1-5.2% higher than other single models. ( Fu et al., 2022 ) constructed a model based on multispectral images and SEL, and found that the integrated learning algorithm produced better classification performance than the basic model, with an overall accuracy rate of 1.6-12.7% higher. There are relatively few studies using SEL for fine classification of peanut seed varieties and mildewed peanuts, and there is a lack of comparative research on the classification ability of the SEL algorithm for peanut seeds using the HSI.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Zou

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Classification Performance Analysis Of Stacked Ensemble Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Zou

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…As one of the most productive [1] and biologically significant ecosystems, mangroves have a high economic and ecological value [2][3][4]. Furthermore, mangrove ecosystems are a valuable resource for recreation, education, and scientific research [5].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Sun

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37%~95.25%.

show abstract

Examination of the performance of ASEL and MPViT algorithms for classifying mangrove species of multiple natural reserves of Beibu Gulf, south China

Fu,

He,

Liang

et al. 2023

Ecological Indicators

View full text Add to dashboard Cite

Comparison of RFE-DL and stacking ensemble learning algorithms for classifying mangrove species on UAV multispectral images

Cited by 29 publications

References 42 publications

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Examination of the performance of ASEL and MPViT algorithms for classifying mangrove species of multiple natural reserves of Beibu Gulf, south China

Contact Info

Product

Resources

About