A semantic segmentation network for red tide detection based on enhanced spectral information using HY-1C/D CZI satellite data

Abstract

Efficiently monitoring red tide via satellite remote sensing is pivotal for marine disaster monitoring and ecological early warning systems. Traditional remote sensing methods for monitoring red tide typically rely on ocean colour sensors with low spatial resolution and high spectral resolution, making it difficult to monitor small events and detailed distribution of red tide. Furthermore, traditional methods are not applicable to satellite sensors with medium to high spatial resolution and low spectral resolution, significantly limiting the ability to detect red tide outbreaks in their early stages. Therefore, this study proposes a Residual Neural Network Red Tide Monitoring Model based on Spectral Information Channel Constraints (SIC-RTNet) using HY-1C/D CZI satellite data. SIC-RTNet improves monitoring accuracy through adding three key steps compared to basic deep learning methods. First, the SIC-RTNet introduces residual blocks to enhance the effective retention and transmission of weak surface signal features of red tides. Second, three spectral information channels are calculated using the four wideband channels of the images to amplify the spectral differences between red tide and seawater. Finally, an improved loss function is employed to address the issue of sample imbalance between red tides and seawater. Compared to other models, SIC-RTNet demonstrates superior performance, achieving precision and recall rates of 85.5 % and 95.4 % respectively. The F1-Score is 0.90, and the Mean Intersection over Union (MoU) is 0.90. The results indicate that the SIC-RTNet can automatically identify red tides using high spatial resolution and wideband remote sensing data, which can help the monitoring of marine ecological disasters.