Bilinear Squeeze-and-Excitation Network for Fine-Grained Classification of Tree Species

Tree species classification is beneficial to multiple applications but is difficult because the categories should be discriminated by subtle differences, and the acquisition of class labels is both expensive and time-consuming. In this letter, a bilinear squeeze-and-excitation network (BiSENet) is proposed for fine-grained classification of tree species. First, objects of the remote sensing data are constructed based on superpixel segmentation. Second, a deep neural network (i.e., BiSENet) is constructed and trained to distinguish different tree species. The proposed BiSENet is inspired by the fine-grained image classification, which is more subtle than traditional classification since it classifies the images within a subordinate category. Moreover, the AdaBound optimization method is adopted to obtain the optimal parameters of BiSENet. Experiments on the Haizhu Lake data acquired by the Jilin-1 satellite demonstrate that the proposed method exhibits superior quantitative and qualitative performance than existing state-of-the-art methods.