Affiliation:
1. Gandhigram Rural Institute, India
2. University of Greifswald, Germany
3. M.V. Muthiah Government Arts College for Women, India
4. Galgotias University, India
Abstract
The chapter on aerospace image processing with bio-inspired algorithms explores their application to overcome challenges in aerospace image processing. Aerospace and satellite imaging provides valuable insights for environmental monitoring, urban planning, disaster management, and agriculture. This chapter focuses on bio-inspired algorithms, drawing from natural phenomena like genetic evolution and swarm behavior, emphasizing accurate analysis. Various algorithms, including genetic, particle swarm optimization, ant colony optimization, artificial bee colony, firefly, bat, cuckoo search, and artificial neural networks, are discussed in terms of their principles and adaptation for aerospace image processing. Applications in satellite image enhancement, classification, segmentation, and feature extraction are explored. Bio-inspired algorithms are integrated into aerospace image registration, change detection, and image fusion, improving accuracy. Case studies demonstrate their effectiveness in handling large datasets, reducing complexity, and enhancing accuracy.