Time series analysis of multispectral satellite images using game theory classifier

Usha, S.; Vasuki, S.

doi:10.1016/j.ijleo.2021.167155

Cited by 3 publications

(2 citation statements)

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“…Difference Vegetation Index DVI R nir -R red [44] Optimized Vegetation Index VIplot 1.45 × (R Texture feature extraction methods mainly include statistical methods, such as gray level cooccurrence matrix (GLCM), texture spectrum and geometric methods; model methods of random field model and fractal model methods; signal processing methods and structural analysis methods [46,47]. Among these methods, the GLCM method is an image recognition technology currently recognized by the academic community as an image recognition technique with strong robustness and adaptation characteristics, which can effectively achieve the classification and retrieval of images and maximize the accuracy of remote sensing image classification processing [16,26]. In this study, the texture features from five bands in multispectral images are extracted through the GLCM method, and the extracted texture feature information mainly includes eight indicators of con, cor, dis, ent, hom, mean, sm, and var.…”

Section: Vegetation Indices Abbreviation Calculation Formulas Referencesmentioning

confidence: 99%

“…In addition to spectral models based on spectral reflectance, the rich texture information of the UAV multispectral images has not been widely used in constructing plant nitrogen models. Previous studies have shown that the texture feature can improve the identification of useful spatial features from the original images and enhance the inversion accuracy when retrieving crop parameters [14][15][16]. For example, Jia and Chen [14] established a model using principal component regression analysis for predicting the nitrogen content of winter wheat using UAV image features at a spectral resolution of 0.06 m; the accuracy of the model established by fusing the spectral and texture features (R 2 = 0.68) of UAV multispectral images was improved by more than 10% compared with that established by a single vegetation index (R 2 = 0.66) or texture feature (R 2 = 0.65).…”

Section: Introductionmentioning

confidence: 99%

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Comparison and Transferability of Nitrogen Content Prediction Model Based in Winter Wheat from UAV Multispectral Image Data

Guo¹,

He²,

Huang³

et al. 2023

Drones - Various Applications

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Information about the nitrogen dynamic in wheat is important for improving in-season crop precision nutrient management and cultivated land sustainability. To develop unmanned aerial vehicle (UAV)-based spectral models for an accurate and effective assessment of the plant nitrogen content in the key stages (jointing, booting, and filling) of wheat growth, winter wheat experiment plots in Henan Province, China, were used in this study. Based on the K6 multichannel imager, 5-band (Red, Green, Blue, Red edge, and Near-infrared (Nir)) multispectral images were obtained from a UAV system and used to calculate 20 vegetation indices and 40 texture features from different band combinations. Combining the sensitive spectral features and texture features of the nitrogen content of winter wheat plants, BP neural network (BP), random forest (RF), Adaboost, and support vector machine (SVR) machine learning methods were used to construct plant nitrogen content models, and compared for the model performance and transferability. The results showed that the characteristics of different spectral features were different, but most of them had a partial normal distribution. Compared with spectral features, the distribution of texture features was more discrete. Based on Pearson’s correlation analysis, 51 spectral and texture features were selected to build four machine learning models. The estimates of plant nitrogen by the RF and Adaboost methods were relatively concentrated, mostly close to the 1:1 line; while the estimates of plant nitrogen from the BP and SVR methods were relatively scattered. The RF method was the best, with coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) of 0.811, 4.163, and 2.947 g/m2, respectively; the SVR method was the worst, with R2, RMSE, and MAE of 0.663, 5.348, and 3.956 g/m2, respectively. All models showed strong transferability, especially the RF and Adaboost methods, in predicting winter wheat nitrogen content under rainfed and irrigation water management.

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