Forest type classification using remote sensing data is affected by the limited number of training samples for the classifier owing to the cost of field surveys in practical situations. Although, hyperspectral data can provide rich spectral information, there is a small-sample-size problem with high-dimensional data owing to the limitation number of field samples; thus, the classification model has the risk of poor performance due to model overfitting. Therefore, band selection to avoid overfitting considering the characteristics of the forest environment are required to solve these problems. In this work, we propose a methodology based on sparse discriminant analysis (SDA) using hyperspectral data. It is found that our proposed methodology increased the classification accuracy under the small-sample-size conditions considering practical forest monitoring. Additional training data based on a tree crown density map calculated from very high resolution data further improved classification accuracy of SDA.
['Taichi Takayama', 'Akira Iwasaki']
Selection of additional trainig data for improving accuracy of forest type classification using hyperspectral data