Spatial-Spectral Enhancement and Fusion Network for Hyperspectral Image Classification With Few Labeled Samples

作     者：Liu, Shuang Fu, Chuan Duan, Yule Wang, Xiaopan Luo, Fulin 

作者机构：Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhenzhou 450001 Peoples R China Chongqing Geomat & Remote Sensing Applicat Ctr Chongqing 401147 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING》 (IEEE Trans Geosci Remote Sens)

年 卷 期：2025年第63卷

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：Fundamental Research Funds for the Central Universities [2024CDJYXTD-009] National Natural Science Foundation of China [62371076, 62071340, 42201371] New Chongqing Youth Innovative Talents Project [CSTB2024NSCQ-QCXMX0071] Natural Science Foundation of Chongqing [CSTB2022NSCQ-MSX0452] Joint Fund Project of Science and Technology Research Development Plan of Henan Province Chongqing Performance Incentive of Research Institutions Program [CSTB2023JXJL-YFX0036] 

主　　题：Overfitting Hyperspectral imaging Feature extraction Image classification Training Data models Adaptation models Classification algorithms Transfer learning Semisupervised learning Decision fusion few labeled samples hyperspectral image (HSI) classification spatial-spectral feature enhancement 

摘      要：Deep learning has shown great potential in hyperspectral image (HSI) classification. However, training these models usually requires a large amount of labeled data. Since the collection of pixel-level annotations for HSIs is laborious and time-consuming, developing algorithms that can yield good performance in a small sample size situation is of great significance. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. However, prevalent solutions are unsatisfactory in feature discrimination and model overfitting, which greatly limits their performance. To remedy these drawbacks, we propose a novel spatial-spectral enhancement and fusion network for hyperspectral image classification with few labeled samples, named SSEFN. Specifically, we design a spatial-spectral enhancement strategy (SSES) to boost the feature discrimination from spatial and spectral perspectives, which enables the model to learn more easily with fewer samples. In addition, we propose an adaptive decision fusion (ADF) module to fuse the decisions of all enhanced features. Since each decision prediction may have a different trend of overfitting, combining multiple predictions alleviates the overfitting. Extensive experiments are conducted on four diverse hyperspectral image datasets. The results show that our method significantly outperforms the state-of-the-art approaches on all datasets and demonstrates the effectiveness and superiority of the proposed model for hyperspectral image classification with few labeled samples. Codes are available at https://***/liushuang963/SSEFN.