Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery

Using Remotely Piloted Aircraft and Onboard Processing to Optimize and Expand Data Collection

Remotely piloted aircraft (RPA) have the potential to revolutionize local to regional data collection for geophysicists as platform and payload size decrease while aircraft capabilities increase. In particular, data from RPAs combine high-resolution imagery available from low flight elevations with comprehensive areal coverage, unattainable from ground investigations and difficult to acquire from manned aircraft due to budgetary and logistical costs. Low flight elevations are particularly important for detecting signals that decay exponentially with distance, such as electromagnetic fields. Onboard data processing coupled with high-bandwidth telemetry open up opportunities for real-time and near real-time data processing, producing more efficient flight plans through the use of payload-directed flight, machine learning and autonomous systems. Such applications not only strive to enhance data collection, but also enable novel sensing modalities and temporal resolution. NASAs Airborne Science Program has been refining the capabilities and applications of RPA in support of satellite calibration and data product validation for several decades. In this paper, we describe current platforms, payloads, and onboard data systems available to the research community. Case studies include Fluid Lensing for littoral zone 3D mapping, structure from motion for terrestrial 3D multispectral imaging, and airborne magnetometry on medium and small RPAs

Weathering the Storm: Unmanned Aircraft Systems in the Maritime, Atmospheric and Polar Environments

Remotely piloted aircraft (RPA) have the potential to revolutionize local to regional data collection for geophysicists as platform and payload size decrease while aircraft capabilities increase. In particular, data from RPAs combine high-resolution imagery available from low flight elevations with comprehensive areal coverage, unattainable from ground investigations and difficult to acquire from manned aircraft due to budgetary and logistical costs. Low flight elevations are particularly important for detecting signals that decay exponentially with distance, such as electromagnetic fields. Onboard data processing coupled with high-bandwidth telemetry open up opportunities for real-time and near real-time data processing, producing more efficient flight plans through the use of payload-directed flight, machine learning and autonomous systems. Such applications not only strive to enhance data collection, but also enable novel sensing modalities and temporal resolution. NASAs Airborne Science Program has been refining the capabilities and applications of RPA in support of satellite calibration and data product validation for several decades. In this paper, we describe current platforms, payloads, and onboard data systems available to the research community. Case studies include Fluid Lensing for littoral zone 3D mapping, structure from motion for terrestrial 3D multispectral imaging, and airborne magnetometry on medium and small RPAs

Fluid lensing and machine learning for centimeter-resolution airborne assessment of coral reefs in American Samoa

A novel NASA remote sensing technique, airborne fluid lensing, has enabled cm-resolution multispectral 3D remote sensing of aquatic systems, without adverse refractive distortions from ocean waves. In 2013, a drone-based airborne fluid lensing campaign conducted over the coral reef of Ofu Island, American Samoa, revealed complex 3D morphological, ecological, and bathymetric diversity at the cm-scale over a regional area. In this paper, we develop and validate supervised machine learning algorithm products tailored for accurate automated segmentation of coral reefs using airborne fluid lensing multispectral 3D imagery. Results show that airborne fluid lensing can significantly improve the accuracy of coral habitat mapping using remote sensing.The machine learning algorithm is based on multidimensional naïve-Bayes maximum a posteriori (MAP) estimation. Provided a user-selected training subset of 3D multispectral images, comprising ~1% of the total dataset, the algorithm separates living structure from nonliving structure and segments the coral reef into four distinct morphological classes – branching coral, mounding coral, basalt rock, and sand. The user-selected training data and algorithm classification results are created and verified, respectively, with sub-cm-resolution ground-truth maps, manually generated from extensive in-situ mapping, underwater gigapixel photogrammetry, and visual inspection of the 3D dataset with subject matter experts.The algorithm generates 3D cm-resolution data products such as living structure and morphology distribution for the Ofu Island coral reef ecosystem with 95% and 92% accuracy, respectively. By comparison, classification of m-resolution remote sensing imagery, representative of the effective spatial resolution of commonly-used airborne and spaceborne aquatic remote sensing instruments subject to ocean wave distortion, typically produces data products with 68% accuracy. These results suggest existing methodologies may not resolve coral reef ecosystems in sufficient detail for accurate determination of percent cover of living structure and morphology breakdown.The methods presented here offer a new remote sensing approach enabling repeatable quantitative ecosystem assessment of aquatic systems, independent of ocean wave distortion and sea state. Aquatic remote sensing imagery, free from refractive distortion, appears necessary for accurate and quantitative health assessment capabilities for coral reef ecosystems at the cm-scale, over regional areas. The accurate and automated determination of percent cover and morphology distribution at cm-resolution may lead to a significantly improved understanding of reef ecosystem dynamics and responses in a rapidly-changing global climate.•Airborne Fluid Lensing creates cm-scale 3D images of coral reefs in American Samoa.•A machine learning algorithm is developed to classify coral using these 3D images.•Using the algorithm on cm-scale 3D data, coral cover is measured with 95% accuracy.•The algorithm is validated with cm-resolution maps from in situ reference data