Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images

Airborne hyperspectral remote sensing data with both rich spectral and spatial features can effectively improve the classification accuracy of vegetation species. However, the spectral data of hundreds of bands brings about problems such as dimensional explosion, which poses a huge challenge for hyperspectral remote sensing classification based on classical parameters models. Deep learning methods have been used for remotely sensed images classification in recent years, but the popular HSI datasets including Kennedy Space Center, Indian Pines, Pavia University scene and Salinas scene, have low spatial resolution, significant differences between categories, and regular boundaries. When applied to the classification of forestry tree species, the accuracy often decreases because the spectral response of different plants of the same family and genus are very similar, especially under the fragmented species distribution, complex topography and the occluded canopy. So we collect new data sets, selected Gaofeng State Owned Forest Farm in Guangxi province in south China as the research area and adopted the airborne hyperspectral data obtained by the LiCHy system of the Chinese Academy of Forestry to explore an improved three-dimensional convolutional neural network(3D-CNN) model for tree species classification. The proposed model uses raw data as input without dimension reduction or feature screening, and simultaneously extracts spectral and spatial features. After the 3D convolutional layer, the captured high-level semantic concept is a joint spatial spectral feature representation, so we can turn it into a one-dimensional feature as a new input to learn a more abstract level of expression. The widely used earlystop method is also used to prevent overfitting. The proposed model is a lightweight, generalized, and fast convergence classification model, by which the short-time and large-area of multiple tree species classification with high-precision can be realized. The result shows that the 3D-1D CNN model can shorten the training time of the 3D CNN model by 60% and achieve a classification accuracy of 93.14% within 50 ha in 6.37 min, which provides a basis for the classification of tree species, the mapping of forest form and the inventory of forest resources.