2023/01/01 by Ziqi Zhao, Changbao Yang, Qiong Wu
Chemistry · Engineering · #Advanced Image Fusion Techniques #Remote-Sensing Image Classification #Spectroscopy and Chemometric Analyses
paper · doi:10.1109/tgrs.2023.3307123
openalex publication_date 2023/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Patchwise methods have been widely used in hyperspectral image (HSI) classification. In HSI classification methods that use only spectral information, a large number of unlabeled pixels, known as background pixels, are removed before training, which loses spatial information. Patchwise methods obtain patches through a local window to retain the spectral-spatial information in background pixels. Larger patches have more background pixels, but more redundant information in the extracted high-dimensional features reduces classification accuracy. Due to the complex and uneven distribution of land cover classes, class imbalance is a common problem in HSI. In this paper, a two windows discrete cosine transform and synthetic minority over-sampling technique one-versus-all ensemble classifiers (TwoWin-SOVA) method is proposed for addressing the problems of high dimensionality caused by large patches and class imbalance, which consists of feature extraction and ensemble classifiers. A two windows discrete cosine transform (TwoWin-DCT) is utilized for feature extraction, compressing large patches to extract spectral-spatial features, which achieves dimensionality reduction. Index features, which represent the correlation between spectral bands, are fused with them as spectral-spatial-index features. Synthetic minority over-sampling technique one-versus-all ensemble classifiers (SOVA) construct ensemble classifiers utilizing one-versus-all (OVA) strategy with synthetic minority over-sampling technique (SMOTE) and the base classifiers of light gradient boosting machine (LightGBM) to address class imbalance problem. The experimental results on the five public HSI datasets show that TwoWin-SOVA effectively tackles problems of high dimensionality caused by large patches and class imbalance, achieving classification performance as good as several state-of-the-art methods.