Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

Xu, Yi; Wang, Junjie; Xia, Anquan; Zhang, Kangyong; Dong, Xuanyan; Wu, Kaipeng; Wu, Guofeng

doi:10.3390/rs11030254

Cited by 21 publications

(11 citation statements)

References 86 publications

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“…Wavelet transform is often used for data compression, noise elimination, and information extraction and has been widely used in digital signal processing, seismic wave analysis, hyperspectral image processing, and other fields [25,26,27,28,29].…”

Section: Crop/weed Discriminationmentioning

confidence: 99%

“…The core idea is to project nonlinear problems in a low-dimensional feature space into a highly dimensional feature space, so that the projected data are linearly separable, simplifying the problem of solving linear SVM classification problems. The small-sample learning capability of SVM fits the needs of hyperspectral data processing and, thus, is widely used in hyperspectral remote sensing (spectral imaging) classification [22,23,24,25,26,27,28,29,30,31,32,33,34].…”

Section: Crop/weed Discriminationmentioning

confidence: 99%

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Crop/Weed Discrimination Using a Field Imaging Spectrometer System

Liu

et al. 2019

Sensors

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Nowadays, sensors begin to play an essential role in smart-agriculture practices. Spectroscopy and the ground-based sensors have inspired widespread interest in the field of weed detection. Most studies focused on detection under ideal conditions, such as indoor or under artificial lighting, and more studies in the actual field environment are needed to test the applicability of this sensor technology. Meanwhile, hyperspectral image data collected by imaging spectrometer often has hundreds of channels and, thus, are large in size and highly redundant in information. Therefore, a key element in this application is to perform dimensionality reduction and feature extraction. However, the processing of highly dimensional spectral imaging data has not been given due attention in recent studies. In this study, a field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed and used to discriminate carrot and three weed species (purslane, humifuse, and goosegrass) in the crop field. Dimensionality reduction was performed on the spectral data based on wavelet transform; the wavelet coefficients were extracted and used as the classification features in the weed detection model, and the results were compared with those obtained by using spectral bands as the classification feature. The classification features were selected using Wilks’ statistic-based stepwise selection, and the results of Fisher linear discriminant analysis (LDA) and the highly dimensional data processing-oriented support vector machine (SVM) were compared. The results indicated that multiclass discrimination among weeds or between crops and weeds can be achieved using a limited number of spectral bands (8 bands) with an overall classification accuracy of greater than 85%. When the number of spectral bands increased to 15, the classification accuracy was improved to greater than 90%; further increasing the number of bands did not significantly improve the accuracy. Bands in the red edge region of plant spectra had strong discriminant capability. In terms of classification features, wavelet coefficients outperformed raw spectral bands when there were a limited number of variables. However, the difference between the two was minimal when the number of variables increased to a certain level. Among different discrimination methods, SVM, which is capable of nonlinear classification, performed better.

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Section: Crop/weed Discriminationmentioning

confidence: 99%

Section: Crop/weed Discriminationmentioning

confidence: 99%

Crop/Weed Discrimination Using a Field Imaging Spectrometer System

Liu

et al. 2019

Sensors

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“…4, 6, 27, 39, 40, 48, 44, 45, 57, 68, 97, and 154) were eliminated, and a total of 169 samples of larch wood were obtained. The sample set partitioning based on the joint x–y distances (SPXY) method ( Xu et al., 2019 ) was used to divide the four groups of datasets into the correction set and prediction set. Among them, the calibration set and prediction set had 118 and 51 samples, respectively.…”

Section: Methodsmentioning

confidence: 99%

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

Zhang

Liu

2022

Front. Plant Sci.

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Wood density, as a key indicator to measure wood properties, is of weighty significance in enhancing wood utilization and modifying wood properties in sustainable forest management. Visible–near-infrared (Vis-NIR) spectroscopy provides a feasible and efficient solution for obtaining wood density by the advantages of its efficiency and non-destructiveness. However, the spectral responses are different in wood products with different moisture content conditions, and changes in external factors may cause the regression model to fail. Although some calibration transfer methods and convolutional neural network (CNN)-based deep transfer learning methods have been proposed, the generalization ability and prediction accuracy of the models still need to be improved. For the prediction problem of Vis-NIR wood density in different moisture contents, a deep transfer learning hybrid method with automatic calibration capability (Resnet1D-SVR-TrAdaBoost.R2) was proposed in this study. The disadvantage of overfitting was avoided when CNN processes small sample data, which considered the complex exterior factors in actual production to enhance feature extraction and migration between samples. Density prediction of the method was performed on a larch dataset with different moisture content conditions, and the hybrid method was found to achieve the best prediction results under the calibration samples with different target domain calibration samples and moisture contents, and the performance of models was better than that of the traditional calibration transfer and migration learning methods. In particular, the hybrid model has achieved an improvement of about 0.1 in both R2 and root mean square error (RMSE) values compared to the support vector regression model transferred by piecewise direct standardization method (SVR+PDS), which has the best performance among traditional calibration methods. To further ascertain the generalizability of the hybrid model, the model was validated with samples collected from mixed moisture contents as the target domain. Various experiments demonstrated that the Resnet1D-SVR-TrAdaBoost.R2 model could predict larch wood density with a high generalization ability and accuracy effectively but was computation consuming. It showed the potential to be extended to predict other metrics of wood.

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“…However, the CWA method also has limitations. WF extraction using CWA is based on a global statistical search process and is not a physical process, so generalizing WFs relies heavily on a uniformly distributed and representative training sample [13,25,51]. In addition, the effects of the wheat fertility period and variety were not considered.…”

Section: Detection Model Of Fusarium Head Blightmentioning

confidence: 99%

“…In addition to these traditional spectral features (SFs), continuous wavelet analysis (CWA), as a new tool for signal processing and analysis, has also been applied to hyperspectral information extraction [13,14]. Zhang et al [15] successfully used CWA and partial least squares regression to estimate the severity of powdery mildew disease at the leaf level.…”

Section: Introductionmentioning

confidence: 99%

Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

Huang

et al. 2021

Agriculture

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Fusarium head blight, caused by a fungus, can cause quality deterioration and severe yield loss in wheat. It produces highly toxic deoxynivalenol, which is harmful to human and animal health. In order to quickly and accurately detect the severity of fusarium head blight, a method of detecting the disease using continuous wavelet analysis and particle swarm optimization support vector machines (PSO-SVM) is proposed in this paper. First, seven wavelet features for fusarium head blight detection were extracted using continuous wavelet analysis based on the hyperspectral reflectance of wheat ears. In addition, 16 traditional spectral features were selected using correlation analysis, including two continuous removal transformed spectral features, six differential spectral features, and eight vegetation indices. Finally, wavelet features and traditional spectral features were used as input features to construct fusarium head blight detection models in combination with the PSO-SVM algorithm, and the results were compared with those obtained using random forest (RF) and a back propagation neural network (BPNN). The results show that, under the same feature variables, the PSO-SVM detection method gave an overall higher accuracy than the BPNN detection method, while the overall accuracy of the RF detection model was the lowest. The overall accuracy of the RF, BPNN and PSO-SVM detection models with wavelet features was higher by 3.7%, 2.9% and 8.3% compared to the corresponding methodological models with traditional spectral features. The detection model with wavelet features combining the PSO-SVM algorithm gave the highest overall accuracies (93.5%) and kappa coefficients (0.903) in the six monitoring models. These results suggest that the PSO-SVM algorithm combined with continuous wavelet analysis can significantly improve the accuracy of fusarium head blight detection on the wheat ears scale.

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Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

Cited by 21 publications

References 86 publications

Crop/Weed Discrimination Using a Field Imaging Spectrometer System

Crop/Weed Discrimination Using a Field Imaging Spectrometer System

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

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