Suitable area selection method based on scene matching level segmentation

The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology. This study addresses the challenge of identifying suitable reference image ranges for precise scene matching, which is crucial for enhancing aircraft positioning accuracy. Traditional methods for image matchability analysis are often limited by their reliance on manual feature parameter design and threshold-based filtering, resulting in suboptimal accuracy and efficiency. This paper proposes a novel network architecture for selecting suitable navigation areas using image Matching Level Segmentation (MLSNet). The approach involves two key innovations: a method for generating segmentation labels that quantify matchability levels and an end-to-end network architecture for rapid and precise prediction of reference image matchability segmentation maps. The network includes two core modules: the saliency analysis module uses multi-layer convolutional networks to accurately detect image saliency features across various levels and scales; the multidimensional attention module utilizes attention mechanisms to focus on feature channels and spatial neighborhood scenes to assess the image’s matchability. Our method was rigorously tested on an extensive collection of remote sensing images, where it was benchmarked against a range of both traditional and cutting-edge deep learning methods. The findings indicate that MLSNet is significantly superior to traditional methods in accuracy and efficiency of matchability analysis, and is also relatively ahead of state-of-the-art deep learning models.