A methodology of forest monitoring from hyperspectral images with sparse regularization

This paper presents a methodology to extract information on existing conditions of a forest from hyperspectral images and SAR images for the forest management. To overcome the difficulties in hyperspectral image analysis such as optimal band selection and model overfitting, a machine learning technique called sparse regularization was adopted. Experimental results show the effectiveness of this approach.