Swarm Intelligence Inspired Classifiers in Comparison with Fuzzy and Rough Classifiers: A Remote Sensing Approach

Bansal, Shishir; Gupta, Daya; Panchal, V. K.; Kumar, S Siva

doi:10.1007/978-3-642-03547-0_27

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…In our updating technique, we employed particle swarm optimization (PSO) due to its reliable global optimization capacities and flexibility in inputs and objective functions (see Section 2.2.3). PSO has seen various applications in remote sensing, frequently in image segmentation and classification [21][22][23], but also in agricultural applications. Guo et al, for example, used the algorithm to couple the PROSAIL canopy reflectance model with the WheatGrow crop model based on vegetation indices [24].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization

Wagner

Slawig

Taravat

et al. 2020

IJGI

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A growing world population, increasing prosperity in emerging countries, and shifts in energy and food demands necessitate a continuous increase in global agricultural production. Simultaneously, risks of extreme weather events and a slowing productivity growth in recent years has caused concerns about meeting the demands in the future. Crop monitoring and timely yield predictions are an important tool to mitigate risk and ensure food security. A common approach is to combine the temporal simulation of dynamic crop models with a geospatial component by assimilating remote sensing data. To ensure reliable assimilation, handling of uncertainties in both models and the assimilated input data is crucial. Here, we present a new approach for data assimilation using particle swarm optimization (PSO) in combination with statistical distance metrics that allow for flexible handling of model and input uncertainties. We explored the potential of the newly proposed method in a case study by assimilating canopy cover (CC) information, obtained from Sentinel-2 data, into the AquaCrop-OS model to improve winter wheat yield estimation on the pixel-and field-level and compared the performance with two other methods (simple updating and extended Kalman filter). Our results indicate that the performance of the new method is superior to simple updating and similar or better than the extended Kalman filter updating. Furthermore, it was particularly successful in reducing bias in yield estimation.

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Section: Introductionmentioning

confidence: 99%

Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization

Wagner

Slawig

Taravat

et al. 2020

IJGI

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“…When the remote sensing image is acquired by a UAV, the multispectral image has a certain distortion and deviation due to the atmospheric non-uniformity and the sensor itself. Therefore, when applying a multispectral image, it must be preprocessed first [27][28][29][30]. The main method is to use image control points and image processing algorithms to correct the image.…”

Section: Remote Sensing Image Processingmentioning

confidence: 99%

Research on Assimilation of Unmanned Aerial Vehicle Remote Sensing Data and AquaCrop Model

Li,

Awais

et al. 2024

Sensors

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Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R2 of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R2 and RMSE were 0.927 and 0.12, respectively. The canopy coverage R2 of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing.

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“…Instead of simulating the evolution of organisms, swarm intelligence focuses on the interaction among several agents and their environment. In literature, a large number of swarm optimization approaches have been discussed [4,34], and many researchers have presented different taxonomies [21]. Ant colony optimization (ACO) [11] is a metaheuristic used to find approximate solutions to many optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Introduction to Evolutionary Computation

Ventura

Luna

2016

Pattern Mining With Evolutionary Algorithms

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This chapter presents an overview on evolutionary computation, introducing its basic concepts and serving as a starting point for an inexpert user in this field. Then, the chapter discusses paradigms such as genetic algorithms and genetic programming, which are the most widely used techniques in the mining of patterns of interest. Finally, a brief description about other bio-inspired algorithms is considered, paying special interest in ant colony optimization. The main goal is to help the reader to comprehend some basic principles of evolutionary algorithms and swarm intelligence so next chapters of this book can be understood in an appropriate way.

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Swarm Intelligence Inspired Classifiers in Comparison with Fuzzy and Rough Classifiers: A Remote Sensing Approach

Cited by 5 publications

References 9 publications

Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization

Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization

Research on Assimilation of Unmanned Aerial Vehicle Remote Sensing Data and AquaCrop Model

Introduction to Evolutionary Computation

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