Self-supervised disturbing feature reconstruction network for mangrove biomass estimation with limited data

Abstract

Accurate estimation of mangrove biomass is significant for ensuring the mangrove ecosystem’s productivity and global carbon cycling. Although well-known deep neural networks (DNNs) have been successfully applied in mangrove biomass estimation using remote sensing data, the key problem of data scarcity is not addressed very well for existing methods. Thus, a novel DNN called self-supervised disturbing feature reconstruction network (SSDFRN) is constructed in this article for mangrove biomass estimation with limited data. First, a disturbing feature reconstruction-based self-supervised learning (DFRSSL) method based on random feature shuffle and disturbing feature reconstruction is proposed for solving the data scarcity problem. In addition, a multi-view convolutional neural network (MVCNN) is constructed by stacking several multi-view cascaded convolution modules (MVCCMs), which effectively enhances feature learning performance and improves mangrove biomass estimation accuracy. The mangrove biomass dataset obtained from Ximen Island (28° 21′ N, 121° 10′ E) is used in this study to verify the outperformance of SSDFRN. The experimental results illustrate that SSDFRN is effective in deep feature learning and mangrove biomass estimation with limited data.