Dynamic Tracking Matched Filter with Adaptive Feedback Recurrent Neural Network for Accurate and Stable Ship Extraction in UAV Remote Sensing Images - Dongyang Fu; Shangfeng Du; Yang Si; Yafeng Zhong; Yongze Li (Remote Sensing, 2024)
In an increasingly globalized world, the intelligent extraction of maritime targets is crucial for both military defense and maritime traffic monitoring. The flexibility and cost-effectiveness of unmanned aerial vehicles (UAVs) in remote sensing make them invaluable tools for ship extraction. Therefore, this paper introduces a training-free, highly accurate, and stable method for ship extraction in UAV remote sensing images. First, we present the dynamic tracking matched filter (DTMF), which leverages the concept of time as a tuning factor to enhance the traditional matched filter (MF). This refinement gives DTMF superior adaptability and consistent detection performance across different time points. Next, the DTMF method is rigorously integrated into a recurrent neural network (RNN) framework using mathematical derivation and optimization principles. To further improve the convergence and robust of the RNN solution, we design an adaptive feedback recurrent neural network (AFRNN), which optimally solves the DTMF problem. Finally, we evaluate the performance of different methods based on ship extraction accuracy using specific evaluation metrics. The results show that the proposed methods achieve over 99% overall accuracy and KAPPA coefficients above 82% in various scenarios. This approach excels in complex scenes with multiple targets and background interference, delivering distinct and precise extraction results while minimizing errors. The efficacy of the DTMF method in extracting ship targets was validated through rigorous testing.