Object counting in remote sensing via selective spatial‐frequency pyramid network

Abstract

The integration of remote sensing object counting in the Mobile Edge Computing (MEC) environment is of crucial significance and practical value. However, the presence of significant background interference in remote sensing images poses a challenge to accurate object counting, as the results are easily affected by background noise. Additionally, scale variation within remote sensing images presents a further difficulty, as traditional counting methods face challenges in adapting to objects of different scales. To address these challenges, we propose a selective spatial‐frequency pyramid network (SSFPNet). Specifically, the SSFPNet consists of two core modules, namely the pyramid attention (PA) module and the hybrid feature pyramid (HFP) module. The PA module accurately extracts target regions and eliminates background interference by operating on four parallel branches. This enables more precise object counting. The HFP module is introduced to fuse spatial and frequency domain information, leveraging scale information from different domains for object counting, so as to improve the accuracy and robustness of counting. Experimental results on RSOC, CARPK, and PUCPR+ benchmark datasets demonstrate that the SSFPNet achieves state‐of‐the‐art performance in terms of accuracy and robustness.

Special Issue on “Ensuring security for artificial intelligence applications in mobile edge computing software systems”

“…In the eighth article, Chen et al 8 introduced a selective spatial-frequency pyramid network (SSFPNet) to tackle the challenges faced during background interference and scale variation in remote sensing images, which features two key modules: the Pyramid Attention (PA) module and the Hybrid Feature Pyramid (HFP) module. PA module improves accuracy by precisely extracting target regions and reducing background noise using four parallel branches.…”