Comparative Review of Remote Sensing Methods for Ocean Plastic Litter Detection

Marine ecosystems are significantly threatened by plastic litter, hence, effective detection and monitoring techniques are necessary. The identification and classification of ocean plastic litter is made possible by the development of remote sensing (RS) methods such as optical imaging, thermal infrared (TIR) sensing, hyperspectral imaging (HSI), multispectral imaging (MSI), and synthetic aperture radar (SAR). This article reviews the RS methods highlighting their unique strengths and limitations, emphasizing on their application contexts, data processing requirements, and potential integration for enhanced detection accuracy. It also investigates marine plastic characteristics such as size, specific gravity, spectral characteristics, thermal emissivity, surface roughness, and dielectric properties required for effective ocean plastic litter detection. The study shows different RS data collection methods, platforms used, applications, locations of study, data classes, dataset availability, benefits, and limitations. The raw data obtained by the various RS methods are susceptible to unwanted signals such as atmospheric conditions, cloud cover, and sunglint. Therefore, a detailed review of the different data preprocessing methods for RS data such as atmospheric correction, data fusion, noise reduction, cloud masking, sunglint correction, resampling, and image enhancement are performed. This contributes to enhancing the identification and monitoring of ocean plastic litter. The study further introduces a systematic review of existing ocean plastic litter detection methods based on RS, and shows the detailed process of data collection, preprocessing, data analysis, validation, mapping, and reporting. There are other factors contributing to the effective detection of ocean plastic litter using the RS methods such as sensor resolution, ground truth data, and classification methods which are discussed. Finally, the article investigates the sensor resolution, ground truth data, and classification methods. The study identifies significant gaps in the existing literature and recommends integrating multiple RS methods and optimizing preprocessing techniques to enhance ocean plastic litter detection.