An Adaptive Neuro-Fuzzy Approach for Decomposition of Mixed Pixels to Improve Crop Area Estimation Using Satellite Images

Dwivedi, Arun Kant; Roy, Sudip; Singh, Dharmendra

doi:10.1109/igarss39084.2020.9323128

Cited by 5 publications

(1 citation statement)

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“…An artificial neural network (ANN) is widely employed to classify satellite images in various land cover classes [33], [34]. In [35], an adaptive neuro-fuzzy (ANF) algorithm is developed for mixed pixel decomposition, wherein exponential normalized output of neural network is used as a membership criterion of each class for a mixed pixel. But exponential normalized output of neural network is not exact same as fractional values of all membership classes of mixed pixel.…”

Section: Introductionmentioning

confidence: 99%

Development of an Adaptive Linear Mixture Model for Decomposition of Mixed Pixels to Improve Crop Area Estimation Using Artificial Neural Network

Dwivedi

Singh

et al. 2023

IEEE Access

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Precise spatial information of crop distribution is vital for government and research organizations to monitor agriculture activities like crop health monitoring, crop yield prediction, and food security. Mapping of crop area is challenging in smallholder farming like India, where crop parcels are smaller than two hectares. With an extension of artificial intelligence, an artificial neural network has ability to learn the spectral feature of multispectral satellite images and map them to a land cover class. However, mixed pixel is a challenging problem in pixel wise classification of coarse resolution satellite images. The linear mixture model is successfully utilized to unmix the signals of a mixed classes. The success of linear mixture model is depending on the selection of endmembers of a mixed class. Therefore, this paper presents an adaptive approach for automatic selection of endmembers of a mixed pixel in linear mixture model using spectral and spatial information. The proposed approach is capable of extracting the fraction area cover of each class by using a constrained least-squares error solution. The GPS field surveys, and drone images are employed to create reference data for the accuracy assessment of proposed algorithm. The experimentation results indicate that the solution of the proposed approach outperformed recent baseline methods in terms of efficiency and accuracy of pixelwise estimated area and overall estimated area of various land cover classes.INDEX TERMS Smallholder farming, area estimation, artificial neural network, linear mixture model, mixed pixels, precision agriculture monitoring.

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Section: Introductionmentioning

confidence: 99%