Hyperspectral Data Classification and Regression Using Wavelet Transform

Abstract

Since hyperspectral data is composed of many spectral bands, it is used for classification with high accuracy. Various studies using spatial information as well as spectral information have been conducted to improve accuracy. However, in recent years pixel-based methods have been reviewed for purposes such as anomaly detection, which is originally expected for hyperspectral data. In this work, we applied one of the time-frequency analysis techniques, wavelet transform, to improve the information extraction capability from hyperspectral data in both classification and regression tasks. As the result, the proposed method showed higher accuracy for classification compared to the conventional methods without spatial information. Also, it was confirmed that the proposed method was effective even for small size classes. For regression, we compared various regression models and confirmed the effectiveness of the proposed method for almost all models.