Aircraft Target Recognition in Visible Light Images - Fuzzy Difference Method

We implement a fuzzy difference method that enhances edge information of targets in images by calculating pixel intensity variations through Gaussian blur operations at different scales. This approach makes target contours more distinct and detectable.

An improved adaptive threshold segmentation algorithm dynamically adjusts threshold values based on local image characteristics using statistical analysis of pixel intensity distributions. This enables more precise separation of targets from complex backgrounds through region-based adaptive thresholding.

We employ seventh-order Hu moment invariants for shape feature extraction, which provides rotation, scale, and translation invariant descriptors. These moment features are calculated using central moment computations and offer robust characteristics for target identification and classification tasks.

By integrating these methods systematically, we achieve significant improvements in both accuracy and reliability of aircraft target recognition in visible light imagery. The combination of enhanced edge detection, adaptive segmentation, and invariant feature descriptors creates a comprehensive recognition framework.