Coastal Marine Debris Mapping Using Multi-Modal Feature Extraction Pipeline

Marine debris in the ocean is becoming an increasing problem for the management of coastal oceans and seaside resort areas. This paper presents a method for coastal marine debris mapping using satellite images from multiple satellite platforms. We carry out a pilot project in association with a local government to collect in-situ measurements of debris deposited on beaches and download the coincident satellite images to identify the marine debris. We propose to study the detection of marine debris on land and in the coastal ocean with various sources of satellite imagery as a way to increase the revisit frequency. High temporal resolution data can provide an agile estimation of the resources required to mitigate the pollution accumulation on the shoreline. A major challenge of monitoring specific areas from optical satellite images is the obscuration by cloud cover, which makes it decrease the sampling frequency. To get a handle on this problem and establish high-fidelity model, we acquired the greatest number of satellite images from a variety of platforms including high temporal-resolution imagery provided by small satellite constellation programs. We first established our method using entropy of the segmentation model output on marine debris mapping in coastal areas using WorldView images provided by MAXAR corp. Then we extended the pipeline to other small satellite images using unsupervised domain adaptation techniques. We showed that the spatial representation of the segmentation map is greatly improved by the domain adaptation techniques. Whereas some dataset still requires more data samples and additional quantitative analysis, we confirmed the compatibility of the segmentation output to the established pipeline using entropy metrics to estimate the accumulation density of the debris. This analysis shows the robust capability to be able to apply the pipeline to different types of satellite images, which can be also applied in other remote sensing applications