Hybrid Segmentation Method for Boundary Delineation of Agricultural Fields in Multitemporal Satellite Image using HS-PSO-FCNN

Abstract

Demonstrating the ability to map and track the geographic distribution of agricultural resources is crucial for enhancing agricultural production and guaranteeing food security in numerous parts of the world. A satellite-based technique can significantly cut expenses and boost productivity when compared to traditional field surveys, allowing for the comprehensive mapping of agricultural resources across broad geographic areas. This work focuses on delineating agricultural fields from multispectral satellite images, a field is a piece of land used for agriculture that contains specific land, water, and other features. In this paper, morphological leveling and morphological continuity are utilized to formalize a morphological characteristic. In Multiscale Segmentation (MS), this attribute is defined by taking the derivative of the morphological profile. MS is very beneficial for complicated image scenarios such as aerial or fine-resolution satellite imagery. Based on Hybrid Segmentation of Particle-Swarm-Optimization with a Fully Convolution Network (HS-PSO-FCN), the efficiency of the work has been increased. PSNR processing time has been taken as output metrics used to assess the quality.