ACTL: Asymmetric Convolutional Transfer Learning for Tree Species Identification Based on Deep Neural Network

Abstract

The identification of tree species is of great significance to the sustainable management and utilization of forest ecosystems. Hyperspectral data provide sufficient spectral and spatial information to classify tree species. Convolutional neural networks (CNN) have achieved great success in hyperspectral image (HSI) classification. The outstanding performance of CNN in HSI classification relies on sufficient training samples. However, it's expensive and time consuming to acquire labeled training samples. In this article, a novel asymmetric convolutional transfer learning model for HSI classification is proposed. First, the tree species identification dataset is built from Goddard's LiDAR, Hyperspectral & Thermal (G-LiHT) data. Then, the asymmetric convolutional transfer learning model and weights trained on ImageNet dataset are used to initialize the weights of the HSI classification model. Finally, a well fine-tuned neural network on tree species dataset is used to perform the HSI classification task. The experimental results reveal that the proposed model with asymmetric convolutional blocks effectively improves the accuracy of Howland forest tree species identification and provides a new idea for the classification of hyperspectral remote sensing images.

Keywords

• Deep neural networks
• G-LiHT
• hyperspectral image
• asymmetric convolution block
• transfer learning