A hyperspectral image reconstruction algorithm based on RGB image using multi-scale atrous residual convolution network

Abstract

Hyperspectral images are a valuable tool for remotely sensing important characteristics of a variety of landscapes, including water quality and the status of marine disasters. However, hyperspectral data are rare or expensive to obtain, which has spurred interest in low-cost, fast methods for reconstructing hyperspectral data from much more common RGB images. We designed a novel algorithm to achieve this goal using multi-scale atrous convolution residual network (MACRN). The algorithm includes three parts: low-level feature extraction, high-level feature extraction, and feature transformation. The high-level feature extraction module is composed of cascading multi-scale atrous convolution residual blocks (ACRB). It stacks multiple modules to form a depth network for extracting high-level features from the RGB image used as an input. The algorithm uses jump connection for residual learning, and the final high-level feature combines the output of the low-level feature extraction module and the output of the cascaded atrous convolution residual block element by element, so as to prevent gradient dispersion and gradient explosion in the deep network. Without adding too many parameters, the model can extract multi-scale features under different receptive fields, make better use of the spatial information in RGB images, and enrich the contextual information. As a proof of concept, we ran an experiment using the algorithm to reconstruct hyperspectral Sentinel-2 satellite data from the northern coast of Australia. The algorithm achieves hyperspectral spectral reconstruction in 443nm-2190nm band with less computational cost, and the results are stable. On the Realworld dataset, the reconstruction error MARE index is less than 0.0645, and the reconstruction time is less than 9.24S. Therefore, in the near infrared band, MACRN reconstruction accuracy is significantly better than other spectral reconstruction algorithms. MACRN hyperspectral reconstruction algorithm has the characteristics of low reconstruction cost and high reconstruction accuracy, and its advantages in ocean spectral reconstruction are more obvious.

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“…In recent years, with the enrichment of hardware equipment and data, data-driven methods have gradually become the mainstream for hyperspectral reconstruction, for example in textile, agriculture, and remote sensing industries [15][16][17][18]. Convolutional Neural Networks (CNN)-based methods like the HSCNN-D [19] approach integrate path expansion through a dense structure, concatenating the outputs of convolutional kernels across various scales, thereby surpassing the earlier version, HSCNN [20].…”