A Method for Updating Individual Tree Species Samples Based on Different Images

The correct classification of individual tree species (ITS) is of great importance for forest resource management. However, current ITS samples collection is time-consuming and laborious in field surveys, and the samples cannot be updated in time. In this study, a sample update method is proposed, which updates samples by detecting the spectral angular similarity of samples at different times at the same location, enabling the reuse of historical samples and timely update of samples. Experimental results showed that the classification accuracy could reach more than 90% by using DenseNet model and the updated samples, which proved that the sample update method could achieve sample update and sample reuse effectively. In addition, there was significant improvement in the classification accuracy of the sample set using multispectral and texture information fusion, proving the effectiveness of information fusion in increasing classification information.