Conv-J2SMamba: A New Hybrid Convolutional and Joint Scan Merge Structured Mamba Network for Hyperspectral Image Classification

Conv-J2SMamba: A New Hybrid Convolutional and Joint Scan Merge Structured Mamba Network for Hyperspectral Image Classification | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 September 2025 V1 Latest version Share on Conv-J2SMamba: A New Hybrid Convolutional and Joint Scan Merge Structured Mamba Network for Hyperspectral Image Classification Authors : Lianhui Liang 0000-0001-6958-0443 [email protected] , Peiyi Xie , Ying Zhang , Shaoquan Zhang , Thomas Xinzhang Wu , Jun Li , and Antonio Plaza Authors Info & Affiliations https://doi.org/10.22541/au.175683243.33432270/v1 239 views 165 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Recently, Mamba has shown great potential in longsequence modeling and begun to be applied to hyperspectral image classification (HSIC). However, as a causal sequence model, Mamba can only obtain a global receptive field at the end of the sequence, which limits its ability to capture complex spatial structures in hyperspectral images (HSIs). To address this problem, a new Hybrid Convolution and Joint Scan Merge Structured Mamba Network (Conv-J2SMamba) is proposed for HSIC, which establishes neighborhood connectivity within the state space model to extract global-local features from HSIs. Specifically, our Joint Scan Merge Structured Mamba (J2SMamba) is designed to extract global features, while convolution captures local features. The core module of J2SMamba is the Joint Scan Merge Structure-Aware State Space Model (J2-SASSM), which introduces two key innovations. First, the Joint Scan Merge strategy concatenates dual HSIs and constructs four stride-based scanning paths for joint scanning and merging. This strategy enables omnidirectional global feature extraction. Second, the Structure-Aware State Space Model (SASSM) utilizes the Structure-Aware State Fusion (SASF) equation to capture spatial structural dependencies in the HSI, significantly enhancing the flow of contextual information. To better integrate the generated features, a Contrastive Guided Feature Fusion (CGFF) module is also introduced, which employs a multi-level attention mechanism to guide key feature enhancement and multi-level feature fusion. Experimental results on three public HSI datasets demonstrate that the proposed Conv-J2SMamba model outperforms state-of-the-art methods. Supplementary Material File (conv_j2smamba__tmm_.pdf) Download 2.61 MB Information & Authors Information Version history V1 Version 1 02 September 2025 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License Keywords contrast guided feature fusion (cgff) module hyperspectral image classification (hsic) joint scan merge signal processing and analysis structure-aware state space model (sassm) Authors Affiliations Lianhui Liang 0000-0001-6958-0443 [email protected] View all articles by this author Peiyi Xie View all articles by this author Ying Zhang View all articles by this author Shaoquan Zhang View all articles by this author Thomas Xinzhang Wu View all articles by this author Jun Li View all articles by this author Antonio Plaza View all articles by this author Metrics & Citations Metrics Article Usage 239 views 165 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lianhui Liang, Peiyi Xie, Ying Zhang, et al. Conv-J2SMamba: A New Hybrid Convolutional and Joint Scan Merge Structured Mamba Network for Hyperspectral Image Classification. Authorea . 02 September 2025. DOI: https://doi.org/10.22541/au.175683243.33432270/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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