Macroalgae monitoring from satellite optical images using Context-sensitive level set (CSLS) model. (May 2023) - Pan, Xinliang; Meng, Dongdong; Ren, Peng; Xiao, Yanfang; Kim, Keunyong; Mu, Bing; Tao, Xuanwen; Liu, Rongjie; Wang, Quanbin; Ryu, Joo-Hyung; Cui, Tingwei
Highlights: A novel model for satellite macroalgae monitoring. Superior to widely-adopted methods. Applicable to multi-source satellite images. Accommodate complex atmosphere ocean environment. Abstract: Automatically detecting macroalgae from optical remote sensing images has been a long-standing problem in ocean monitoring. Existing operational spectral-based techniques tend to assign a threshold to an indicator such as Normalized Difference Vegetation Index (NDVI) to determine whether or not macroalgae exists in a pixel. This thresholding scheme relies on individual pixel features and cannot address the environment variability among different images or even across one single image. To address these limitations, we develop the unsupervised classification method taking the ecological indicator of NDVI as input by exploiting a region-based level set driven by context-sensitive energy function for macroalgae detection from optical remote sensing images. Quantitative and qualitative evaluations based on satellite optical images with various imaging time and spatial resolutions (16–250 m) from GF-1, GF-4, Landsat-8, and Aqua demonstrate the effectiveness and robustness for Ulva prolifera detection. The model is proved to be superior to the traditional NDVI thresholding method and state-of-the-art level set methods in terms of higher accuracy ( Kappa coefficients 0.87–0.92) and automation (without artificial threshold/parameter adjustment). Moreover, the model has the advantage of accommodating complex environmental conditions (such as different water turbidity, thin clouds and sun glint), and can be utilized for detection of Sargassum bloom. The model could facilitate the accurate extraction of macroalgae information and thus serve disaster prevention and control. It may also be used to assist high-accuracy training data preparation for supervised classification and detect the objects with obvious texture and spectral characteristics (such as oil spills) based on customized bands combination.