Classification of Individual Tree Species in High-Resolution Remote Sensing Imagery Based on Convolution Neural Network

Tree species investigation has been faced with problems such as high cost, low efficiency, and low precision. The use of remote sense can greatly increase the work efficiency of tree species investigation and save cost. Although convolutional neural network (CNN) has made many breakthroughs in natural image classification area, few people have used CNN model to carry out individual tree species classification. Based on the above considerations, this paper builds CNN models, and integrates them with high-resolution remote sensing imagery to classify individual tree species. In the course of semi -automatically constructing the sample set of remote sensing imagery of individual tree species with high-resolution imagery, the crown slices from imagery (CSI) delineation, manual annotation, and data augmentation are used. Meanwhile, in order to train the sample set of remote sensing imagery of individual tree species, five CNN models are adapted. Through comparative analysis, it is found that LeNet5_relu and AlexNet_mini cannot achieve the best classification effect. GoogLeNet_mini56, ResNet_mini56, and DenseNet_BC_mini56 have the best classification effect for different species respectively. DenseNet_BC_mini56 has the highest overall accuracy (94. 14%) and the highest Kappa coefficient (0. 90), making it the best classification model from all aspects. The research proves the effectiveness of CNN in the classification of individual tree species, which can provide a critical solution for forest resource investigation.