Affiliation:
1. School of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
2. School of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Abstract
Although extensive research shows that CNNs achieve good classification results in HSI classification, they still struggle to effectively extract spectral sequence information from HSIs. Additionally, the high-dimensional features of HSIs, the limited number of labeled samples, and the common sample imbalance significantly restrict classification performance improvement. To address these issues, this article proposes a double-branch multi-scale dual-attention (DBMSDA) network that fully extracts spectral and spatial information from HSIs and fuses them for classification. The designed multi-scale spectral residual self-attention (MSeRA), as a fundamental component of dense connections, can fully extract high-dimensional and intricate spectral information from HSIs, even with limited labeled samples and imbalanced distributions. Additionally, this article adopts a dataset partitioning strategy to prevent information leakage. Finally, this article introduces a hyperspectral geological lithology dataset to evaluate the accuracy and applicability of deep learning methods in geology. Experimental results on the geological lithology hyperspectral dataset and three other public datasets demonstrate that the DBMSDA method exhibits superior classification performance and robust generalization ability compared to existing methods.
Reference57 articles.
1. Current progress of hyperspectral remote sensing in China;Tong;J. Remote Sens.,2016
2. Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations;Meyer;Remote Sens. Environ.,2022
3. Towards resource-frugal deep convolutional neural networks for hyperspectral image segmentation;Nalepa;Microprocess. Microsyst.,2020
4. Kuras, A., Brell, M., Liland, K., and Burud, I. (2023). Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment. Remote Sens., 15.
5. Research and application of UAV-based hyperspectral remote sensing for smart city construction;Yang;Cogn. Robot.,2022
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献