Hybrid Sequence Networks for Unsupervised Water Properties Estimation From Hyperspectral Imagery

• Main Authors: Jiahao Qi, Wei Xue, Zhiqiang Gong, Shaoquan Zhang, Aihuan Yao, Ping Zhong
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Hierarchical multiscale sequence (HMS) loss; hybrid sequence structure; inherent optical properties (IOPs); unsupervised methodology
• Online Access: https://ieeexplore.ieee.org/document/9387097/

Generating accurate estimation of water inherent optical properties (IOPs) from hyperspectral images plays a significant role in marine exploration. Traditional methods mainly adopt bathymetric models and numerical optimization algorithms to deal with this problem. However, these methods usually tend to simplify the bathymetric models and lack the capability of describing the discrepancy between reference spectrum and estimation spectrum, resulting in a limited estimation performance. To get a more precise result, in this work, we propose a novel network based on deep learning to retrieve the IOPs. The proposed network, named as IOPs estimation network (IOPE-Net), explores a hybrid sequence structure to establish IOPs estimation module that acquires high-dimensional nonlinear features of water body spectrums for water IOPs estimation. Moreover, considering the insufficiency of labeled training samples, we design a spectrum reconstruction module combined with classical bathymetric model to train the proposed network in an unsupervised manner. Then, aiming at further promoting the estimation performance, a multicriterion loss is developed as the objective function of IOPE-Net. In particular, we construct a hierarchical multiscale sequence loss as the key component to retain the details of original spectral information. Thus, the discrepancy between different spectrums can be better described by the obtained learning model. Experimental results on both simulated and real datasets demonstrate the effectiveness and efficiency of our method in comparison with the state-of-the-art water IOPs retrieving methods.