D2S2BoT: Dual-Dimension Spectral-Spatial Bottleneck Transformer for Hyperspectral Image Classification
| Autor: | Lan Zhang, Yang Wang, Linzi Yang, Jianfeng Chen, Zijie Liu, Lifeng Bian, Chen Yang |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2655-2669 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1939-1404 2151-1535 |
| DOI: | 10.1109/JSTARS.2023.3342461 |
| Popis: | Hyperspectral image (HSI) classification has become a popular research topic in recent years, and transformer-based networks have demonstrated superior performance by analyzing global semantic features. However, using transformers for pixel-level HSI classification has two limitations: ineffective capture of spatial-spectral correlations and inadequate exploitation of local features. To address these challenges, we propose a dual-dimension self-attention (D2SA) mechanism that fully exploits HIS's high spectral-spatial correlation by using two separate branches to model the global dependence of features from the spectral and spatial dimensions. Additionally, we develop a multilayer residual convolution module that extracts local features and introduces shallow-deep feature interactions to obtain more discriminative representations. Based on these components, we propose a dual-dimension spectral-spatial bottleneck transformer (D2S2BoT) framework for HSI classification that simultaneously models the local interactions and global dependencies of HSI pixels to achieve high-precision classification. By virtue of the D2SA mechanism, the introduced D2S2BOT framework can produce competitive classification results with a limited number of training samples on three well-known datasets, which we hope will provide a strong baseline for future research on transformers in the field of HSI. |
| Databáze: | Directory of Open Access Journals |
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