This is a Python notebook which crops a SAR (Synthetic Aperture Radar) image of a possible oil slick and tries to determine - using simple image statistics - if it's an actual oil spill or a look-alike.
Amanda D., Maria G., José G., Jaelyn Bos, Anand Sekar
This OHW project is the first stage of a greater software project to automate identifying oil spills from satellite imagery called Project SisMOM - Oil Monitoring System at Sea.
The main idea of this project is to automate the process of identifying a possible oil slick in a satellite image, which involves these main steps:
- Loading: Reading the satellite image file(s)
- Cropping: efficient pre-processing, such as a simple statistical analysis (e.g. histogram), to identify possible oiled areas and crop them into patches
- Segmentation: segment the oiled areas from the patches (future)
- Presentation: visualize the segments: make a list, save metadata (future)
The images used were taken from a SAR-2000 imaging sensor on the second COSMO-SkyMed satellite called CSKS2.
#Presentation link: https://docs.google.com/presentation/d/1cOAcY_P9DEm4YivwGf09aQw6VUF08CZnQz67Av50jdw/edit?usp=sharing
- Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review: contains statistics for pre-processing
- An improved semantic segmentation model based on SVM for marine oil spill detection using SAR image: From this July! The introduction to this paper cites some of the other papers in this list and summarizes previous efforts broadly.
- Ocean oil spill detection from SAR images based on multi-channel deep learning semantic segmentation: From this March! The introduction does a more thorough job of summarizing efforts, focusing on deep learning.
- Oil Spill Identification from Satellite Images Using Deep Neural Networks
- Oil Spill Classification Kaggle dataset
- (Book) Automatic Detection Algorithms of Oil Spill in Radar Images
- Oil spill detection by imaging radars: Challenges and pitfalls: focuses on the difficult parts of this effort
- Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions
- A novel deep learning instance segmentation model for automated marine oil spill detection: uses mask-rcnn
- Marine oil spill detection using Synthetic Aperture Radar over Indian Ocean
- A novel deep learning method for marine oil spill detection from satellite synthetic aperture radar imagery: "A large data set consisting of 15,774 labeled oil spill samples derived from 1786C-band Sentinel-1 and RADARSAT-2 vertical polarization SAR images is used to train, validate and test the Faster R-CNN model.”
- A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images
- (META/ Literature Review) Oil Spill Detection and Mapping: A 50-Year Bibliometric Analysis
- Oil Spill Detection Based on Deep Convolutional Neural Networks Using Polarimetric Scattering Information From Sentinel-1 SAR Images
- Feature Merged Network for Oil Spill Detection Using SAR Images
- SAR Oil Spill Detection System through Random Forest Classifiers
- Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training
- Oil spill detection based on texture analysis: how does feature importance matter in classification?
- Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery
- Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection
