Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network

Xin Zhao,Rongjie Liu,Jianqiang Liu,Quanbin Wang,Jing Ding,Yi Ma,Yanfang Xiao

doi:10.3390/rs14010088

Abstract

Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images.

Highlights

Red tides refer to harmful algal blooms (HABs) that constitute a marine ecological disaster resulting from excessive reproduction or accumulation of plankton, protozoa, or bacteria that cause water discoloration [1,2]
The parameters of the fully convolutional neural networks used in this experiment were the same as those of red tide detection U−Net (RDU−Net)
Using an HY−1D CZI image, this study developed a red tide detection framework based on RDU−Net for satellite images with medium to high spatial resolution

Summary

Introduction

Introduction iationsRed tides refer to harmful algal blooms (HABs) that constitute a marine ecological disaster resulting from excessive reproduction or accumulation of plankton, protozoa, or bacteria that cause water discoloration [1,2]. Red tide outbreaks are a threat to fisheries, marine ecosystems, and human health [3,4,5,6]. The dominant phytoplankton which caused red tide in China include dinoflagellates (such as Noctilucent scintillans [7], Prorocentrum donghaiense (P. donghaiense) [8], Alexandrium catenella [9]), and diatom (such as Skeletonema costatum (S. coatatum) [10]). In recent years, red tides have been found to occur more frequently as a result of eutrophication [11,12,13,14]. In 2019 alone, a total of 38 large−scale red tides occurred in China, resulting in direct economic losses of up to USD 4.86 million [15]. Automatic detection and monitoring of red tides is important for red tide prevention and reduction

Methods

Results

Discussion

Conclusion