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

Abstract

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.