文献类型：期刊文献

英文题名：Nonlinear RNN with noise-immune: A robust and learning-free method for hyperspectral image target detection

作者：Xiao, Xiuchun[1];Jiang, Chengze[2];Jin, Long[1];Huang, Haoen[3];Wang, Guancheng[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Southeast Univ, Sch Cyber Sci & Engn, Nanjing 211189, Peoples R China;[3]Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China

年份：2023

卷号：229

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：SCI-EXPANDED(收录号：WOS:001012688500001)、、EI(收录号：20232214173702)、Scopus(收录号：2-s2.0-85160520910)、WOS

基金：This work was supported in part by Natural Science Foundation of Guangdong Province, China, under Grants (2023A1515011477, 2021A1515011847) ; in part by Science and Technology Plan Project of Zhanjiang City, China, under Grant 2022A01063; in part by the Demonstration Bases for Joint Training of Postgraduates of Department of Education of Guangdong Province, China, under Grant 202205; in part by Postgraduate Education Innovation Plan Project of Guang-dong Ocean University, China, under Grants (202250, 202251) ; in part by the Innovation and Entrepreneurship Training Program for College Students of Guangdong Ocean University, China, under Grant 202210566028.

语种：英文

外文关键词：Recurrent neural network; Nonlinear and bounded constraint; Hyperspectral image; Target detection

外文摘要：While the recurrent neural network (RNN) has achieved remarkable performance on dynamic and control tasks, its applications to image processing, particularly target detection are limited. Challenges arise from differences between the two domains, such as the way for merging time information into static problems and variances of dynamic and static solving methods. To this end, we first extend the existing constrained energy minimization (CEM)-based detection scheme to a dynamic version, e.g. dynamic reinforced CEM (DRCEM), which injects the dynamic information. After that, aided by the rigorous mathematical derivation and optimization theory, the DRCEM is merged into the RNN solution framework. To enhance the robustness and convergence of the existing RNN solutions for improving DRCEM performance, the nonlinear and bounded-constraint RNN (NBCRNN) is designed by developing a novel nonlinear activation function, then applying the proposed model to implement the DRCEM scheme. The corresponding theorem results reveal the proposed model possesses global convergence and enhanced robustness. Compared to state-of-the-art works, the DRCEM solved by the NBCRNN model detection method achieves better detection accuracy, with 1.82% improvement in terms of the Kappa coefficient, and reduces the residual error from 10-4 to 10-7. Furthermore, our detection method is able to preserve the detection accuracy in presence of noise perturbated. To the best of our knowledge, it is the first work to develop the zeroing-type RNN for hyperspectral image target detection. The code and models are publicly available at Github DRCEM_NBCRNNCodeImplementation.