Classification and Segmentation of Blooms and Plumes from High Resolution Satellite Imagery Using Deep Learning

Abstract

Recent launches of high-resolution satellite sensors mean Earth Observation data are available at an unprecedented spatial and temporal scale. As data collection intensifies, our ability to inspect and investigate individual scenes for harmful algal or cyanobacterial blooms becomes limited, particularly for global monitoring. Algal Blooms and River Plumes are visible to trained experts in high resolution satellite imagery from Red-Green-Blue composites. Therefore, computer-assisted detection and classification of these events would provide invaluable information to experts and the general public on everyday water use. Advances in image recognition through Deep Learning techniques offer solutions that can accurately detect, classify and segment objects across thousands of images in a fraction of the time a human operator would require, while inspecting these images with much greater detail. Deep Learning techniques that jointly leverage spectral-spatial data are well characterised as a solution to land classification problems and have been shown to be accurate when using multi- or hyper-spectral data such as collected by the Sentinel-2 MultiSpectral Instrument. This work develops on state-of-the-art natural image segmentation algorithms to evaluate the capability of Deep Learning to detect and outline the presence of Algal Blooms or River Plumes in Sentinel 2 MSI data. The challenges in the application of these approaches are highlighted in the availability of suitable training and benchmark data, the use of atmospheric correction and neural network architecture design for utilisation of spectral data. Current Deep Learning network architectures are evaluated to establish best approaches to leverage spectral and spatial data in the context of water classification. Several spectral data configurations are used to evaluate competency and suitability for generalisation to other Optical Satellite Sensor configurations. The impact of the atmospheric correction technique applied to data is explored to establish the most reliable data for use during training and requirements for pre-processing data pipelines. Finally a training dataset and associated Deep Learning method is presented for use in future work relating to water contents classification.