Binary Quantization Vision Transformer for Effective Segmentation of Red Tide in Multispectral Remote Sensing Imagery

As a global marine disaster, red tides pose serious threats to marine ecology and the blue economy, making their monitoring crucial for preventing harmful algal blooms (HABs) and protecting the marine environment. In this study, satellite remote sensing was utilized to provide timely, large-scale, and continuous observation capabilities, overcoming the high cost and spatial and temporal limitations of in situ monitoring. However, existing remote sensing-based methods often exhibit coarse segmentation granularity and suffer from high computational complexity. To overcome these challenges, we propose a novel bimodal multispectral dynamic offset binary quantization visual transformer (DoBi-SWiP-ViT) that utilizes the ViT for global feature aggregation and parameter quantization for efficient segmentation. With the bimodal Swin-ViT with unified perceptual parsing (UPP) architecture, our model integrates data from multiple spectral bands to achieve fine-grained segmentation of large-scale remote sensing images. Additionally, we introduce a dynamic magnitude offset binary quantization ViT block to reduce the parameter redundancy and improve the computational efficiency. In addition, we validated the performance of our model through extensive comparative experiments on high-resolution imagery datasets of sea surface red tides collected from different satellite platforms. The results show that our proposed DoBi-SWiP-ViT has significantly improved the mean accuracy (mAcc) of the segmentation results. For the two test areas acquired from different satellite platforms, the improvements are 8.78% and 10.18%, respectively. This has demonstrated the superior performance of our model in detecting the red tides from high-resolution visible images, highlighting its effectiveness in capturing complex patterns and subtle features in multispectral imagery.