Swarm Intelligence Based on Remote Sensing Image Fusion

Abstract

This chapter presents a remote sensing image fusion based on swarm intelligence. Image fusion is combining multi-sensor images in a single image that has most informative. Remote sensing image fusion is an effective way to extract a large volume of data from multisource images. However, traditional image fusion approaches cannot meet the requirements of applications because they can lose spatial information or distort spectral characteristics. The core of the image fusion is image fusion rules. The main challenge is getting suitable weight of fusion rule. This chapter proposes swarm intelligence to optimize the image fusion rule. Swarm intelligence algorithms are a family of global optimizers inspired by swarm phenomena in nature and have shown better performance. In this chapter, two remote sensing image fusion based on swarm intelligence algorithms, Particle Swarm Optimization (PSO) and flower pollination algorithm are presented to get an adaptive image fusion rule and comparative between them.

IHS Image Fusion Based on Gray Wolf Optimizer (GWO)

“…Many metaheuristics approaches were applied to image fusion to improve the performance of the different image fusion approaches [15]. Solving an optimization problem typically means finding optimal values for the decision variables to maximize or minimize a set of objective functions [13].…”