Hyperspectral image (HSI) classification plays an important role in a wide range of remote sensing applications in military and civilian fields. During past decades, significant efforts have been made on developing datasets and introducing novel approaches to promote HSI classification, such that promising classification performance has been achieved. However, existing datasets generally pose following issues, including the limited categories and annotated samples, the lack of sample diversity, as well as the low spatial resolution. These limitations severely restrict the development and evaluation of data-driven models, especially deep neural network-based ones. In recent years, advances in imaging spectroscopy provide us the opportunity to obtain the hyperspectral image data with high spectral and spatial resolution, therefore, in this paper, we contribute a large-scale benchmark dataset for conducting hyperspectral image classification to address issues raised by existing datasets, noted as ShanDongFeiCheng (SDFC). The proposed SDFC is characterized by (1) The large-scale annotated samples with diverse categories; (2) The high spatial resolution; and (3) The high intra-class variance yet relatively low inter-class variance, making the HSI classification task much more challenging on it. We evaluated 10 classic traditional and deep neural network-based models on SDFC, of which the results can be regarded as useful baselines for further experiments. Moreover, given the state-of-the-art performance of SpectralNet, we selected it as the representation method, and evaluated it across datasets to analyze the difference effects on the classification model induced by different datasets. The comprehensive review and analysis of the representative classification models on both existing and proposed datasets demonstrate the advantages and challenges of our proposed dataset, and provide promising perspectives for future HSI classification studies.
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