Oil spill identification in visible sensor imaging using automated cross correlation with crude oil image filters

An algorithm for detection of crude oil spills in visible light images has been developed and tested on 50 documented crude oil spill images from Shell Petroleum Development Company (SPDC) Nigeria. A set of three 25 x 25 pixels crude oil filters, with unique red, green, and blue (RGB) colour values, homogeneity, and power spectrum density (PSD) features were cross-correlated with the documented spill images. The final crude oil spill Region of Interest (ROI) was determined by grouping interconnected pixels based on their proximity, and only selecting ROIs with an area greater than 5,000 pixels. The crude oil filter cross correlation algorithm demonstrated a sensitivity of 84% with a False Positive per Image (FPI) of 0.82. Future work includes volume estimation of detected spills using crude oil filters, and utilizing this information in the recommendation of appropriate spill clean-up and remediation procedures for the detected spills. Keywords: Crude Oil Spill Detection, Crude oil image filters, Cross correlation, Visible sensor imaging, Oil Spill Segmentation

Condition based Ensemble Deep Learning and Machine Learning Classification Technique for Integrated Potential Fishing Zone Future Forecasting

Artificial Intelligence (AI) technologies have become a popular application in order to improve the sustainability of smart fisheries. Although the ultimate objective of AI applications is often described as sustainability, there is yet no proof as to how AI contributes to sustainable fisheries. The proper monitoring of the longitudinal delivery of different human impacts on activities such as fishing is a major concern today in aquatic conservation. The term "potential fishing zone" (PFZ) refers to an anticipated area of any given sea where a variety of fish may congregate for some time. The forecast is made based on factors including the sea surface temperature (SST) and the sea superficial chlorophyll attentiveness. Fishing advisories are a by-product of the identification procedure. Normalization and preliminary processing are applied to these unprocessed data. The gathered attributes, together with financial derivatives and geometric features, are then utilised to make projections about IPFZ's Technique are used to get the final determination (CECT). In this study, we offer a technique for identifying and mapping fishing activity. Experimentations are performed to validate the efficacy of the CECT method in comparison to machine learning (ML) and deep learning (DL) methods across a variety of measurable parameters. Results showed that CECT obtained 94% accuracy, while Convolutional neural network only managed 92% accuracy on 80% training data and 20% testing data

Combination of satellite imagery and wind data in deep learning approach to detect oil spills

The ocean is vulnerable to oil related activities such as oil production and transport that can harm the environment. Environmental damages from oil spills can be large if not dealt with. Satellite images from radar are useful to detect oil spills because they cover both day and night and penetrates clouds. However, detecting oil spills in ocean areas from satellite images are not a trivial task due to abundance of lookalikes from other natural sources, like river inputs or geological seepage. Auxiliary data such as wind speed in the monitored area, are used to separate oil spills from natural occurring slicks in the manual oil detection process. One solution to detect oil spills is applying artificial intelligence techniques like convolutional neural networks. These convolutional neural networks have usually been a candidate to create an automatic oil detection process. However, the convolutional neural networks have problems with distinguishing between spilled oil spills and look-alikes. This project is about exploring the possibility of detecting oil spills from satellite images and distinguish between spilled oil spills and natural occurring ones by using wind speed data of the area. The convolutional neural network takes in both satellite images and auxiliary wind speed data of the area monitored. Two convolutional neural networks are designed and setup, where one includes auxiliary wind speed data and the other does not. Both CNN’s will have the same satellite images and oil spills to detect such that a direct comparison can be made between them. This work will also be a proof of concept to an automated oil spill detection process that specifically uses wind data in addition to the satellite images. To measure any difference in validation loss, precision or recall by using wind data, both convolutional neural networks are tuned to the same recall such that the false negatives are as low as possible for both neural networks. The comparison between the two neural networks shows that the neural network that includes wind data has 15 % lower validation loss and a slightly higher precision than the neural network that does not include wind data. However, this result is achieved by using wind data generated from the satellite image itself, which metrological wind data is not. A comparison test like this but with metrological wind data instead of wind data generated from the satellite image is considered future work that is worth exploring