Unmanned Aerial Vehicles (UAVs) have been drawing significant attention as effective tools for observing disasters, especially in the context of large-scale disasters, where the establishment of seamless communication channels becomes imperative for verifying safety details and assessing the extent of the disaster. In most instances, insufficient UAV inventories make collaborative efforts among UAVs indispensable for enabling rapid and long-distance data transmission. This paper introduces a UAV data collection model designed for relay transmission of observed data through multi-hop relay communication. It also explores the freshness of data delivery, quantified using the recently developed Age of Information (AoI). Additionally, we derive the formula for calculating AoI and propose a UAV flight scheduling strategy aimed at enhancing the long-term average instantaneous AoI. We demonstrate the performance benefits of this strategy in terms of AoI, average duration, and energy consumption under the proposed framework through a comparison with traditional strategies.

By introducing parameters with local information, several types of orthogonal moments have recently been developed for the extraction of local features in an image. But with the existing orthogonal moments, local features cannot be well-controlled with these parameters. The reason lies in that zeros distribution of these moments' basis function cannot be well-adjusted by the introduced parameters. To overcome this obstacle, a new framework, transformed orthogonal moment (TOM), is set up. Most existing continuous orthogonal moments, such as Zernike moments, fractional-order orthogonal moments (FOOMs), etc. are all special cases of TOM. To control the basis function's zeros distribution, a novel local constructor is designed, and local orthogonal moment (LOM) is proposed. Zeros distribution of LOM's basis function can be adjusted with parameters introduced by the designed local constructor. Consequently, locations, where local features extracted from by LOM, are more accurate than those by FOOMs. In comparison with Krawtchouk moments and Hahn moments etc., the range, where local features are extracted from by LOM, is order insensitive. Experimental results demonstrate that LOM can be utilized to extract local features in an image.