Kendinden denetimli derin öğrenme ile multispektral görüntü eşleştirme

This thesis presents a self-supervised deep learning framework for multispectral image matching, addressing the core challenges posed by nonlinear radiation distortions (NRDs), viewpoint variations across spectral modalities, and the scarcity of annotated datasets. Existing methods often exhibit strong modality dependence and rely heavily on costly supervision, such as depth maps or calibrated camera poses, thereby limiting their generalizability across diverse spectral domains. The framework introduces an improved self-supervision strategy—Improved Multispectral Homographic Adaptation—that enhances pseudo ground truth keypoint generation in cross-spectral settings while ensuring invariance to viewpoint changes. By incorporating a spectrum-aware windowing rule, this method increases both the repeatability and the density of detected keypoints under spectral differences, thereby improving matching performance. This enhancement ultimately leads to more accurate multispectral image registration and is validated on UAV-acquired visible–thermal datasets. Building on this self-supervision strategy, the XPoint framework is proposed as a modular and fully self-supervised image matching architecture. It integrates a pretrained VMamba encoder for robust, modality-invariant feature extraction, alongside lightweight decoder heads for keypoint detection, feature description, and homography regression. This design enables efficient, label-free learning from aligned image pairs and facilitates rapid adaptation across diverse spectral modalities. The framework is designed to be scalable and easily adaptable, requiring no additional supervision beyond image pair alignment. The approach is evaluated across five public benchmarks spanning VIS-TH, VIS-NIR, VIS-LWIR, VIS-SAR datasets. Experimental results demonstrate competitive or superior performance in feature matching and multispectral image registration tasks, while maintaining high computational efficiency. This progression—from spectrum-aware self-supervision to a generalizable matching framework—positions XPoint as a practical solution for real-world multispectral applications, particularly in environments characterized by limited supervision and high spectral variability.