AEO-HGO Adaptive Exploration-Exploitation Hybrid Gas Optimization for Satellite Image Segmentation

Abstract

In recent years, the fast dispensation of high-resolution color satellite images, which is made possible by remote sensing technology, has develop a vital need in key claims such as environmental monitoring, urban planning, and disaster management among other uses. The reason for this is that these apps are very necessary for the success of these applications. Color satellite imaging provides a plethora of information, which enables a more in-depth investigation of land use, plant cover, and other surface features. This is made possible by the use of high-resolution satellite images. In this regard, it is worth noting that there is a vast amount of usage of multi-level image thresholding techniques in an effort to improve the quality of the segmentation process. However, it is worth noting that achieving high accuracy and low processing costs simultaneously in complex scenarios is still a major challenge. This article presents a unique adaptive hybrid optimization technique that is referred to as AEO-HGO. The goal of this optimization method is to address the challenges. The optimization method that has been presented includes a stage of global search that is conducted through the population search, as well as the local search strategy. For the purpose of color multi-level satellite image thresholding, the findings have revealed that the AEO-HGO method has the advantage of providing a solution that is stable, scalable, and computationally efficient. Real-world applications, such as catastrophe management, agricultural monitoring, and urban planning, could potentially benefit from the application of this technique.