IBEWMS: Individual Band Spectral Feature Enhancement-Based Waterfront Environment AAV Multispectral Image Stitching

Abstract

As the use of autonomous aerial vehicles (AAVs) for waterfront monitoring increases, combining multiple AAV multispectral (MS) images into a single, seamless panoramic image has become crucial. This process ensures the accuracy and effectiveness of waterfront monitoring. However, the varying reflective properties of different wavelengths bring challenges for existing single-band MS image stitching frameworks, especially in complex waterfront areas. To address this challenge, we developed the individual band enhanced waterfront multispectral stitching (IBEWMS) framework. Central to this framework is the individual band spectral feature enhancement (IBSFE) module, which enhances each spectral band based on varying reflectance of different land covers, yielding clearer and more reliable features. Using IBSFE, we designed a detector-free framework to effectively extract and match feature points in waterfront MS images. In addition, we implemented an image fusion technique to address issues, such as ghosting and global reflectance inconsistency, in panoramic images. To support this work, we provided the Wuhan AAV Waterfront Environment MS Dataset, comprising 12 315 high-resolution 5-band MS images. Experiments show that IBEWMS outperforms both deep learning and traditional stitching frameworks, offering valuable support for downstream applications.

Keywords

• Feature enhancement
• feature point extraction and matching
• image fusion
• image stitching
• multispectral (MS) images
• waterfront environment