Mangrove Species Mapping Using Deep Learning with Fusion of Hyperspectral and High-Resolution Multispectral Images

Abstract

Accurate mapping of mangroves species is essential for mangrove management, and deep learning of hyperspectral images (HSIs) shows a great advantage in classification with the fine spectrum. However, the sparely available annotations of HSIs are key challenges for accurate mapping using deep learning, especially for mangrove species within small patches. In this work, a high spatial resolution HSI is synthesized using the method of hyperspectral-multispectral image fusion with spectral variability, providing augmented samples as well as spatial information of mangroves. Secondly, the latest 3D convolutional neural network (3DCNN) was investigated to explore spatial and spectral information for mangrove species mapping. Compared to Gaofen 5 using conventional machine learning methods, the synthetic image provides manyfold samples and higher accuracy for mangrove species mapping using 3DCNNs. This work is expected to improve the situation of sample shortage and spatial information deficiency for mangrove species mapping using deep learning with HSIs.