Journal
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Volume 8, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/ijgi8040181
Keywords
spectral information extraction; deep learning; oil film classification
Funding
- Fundamental Research Funds for the Central Universities [3132019143]
- Special Scientific Research Project of Oceanic Public Welfare Profession of China by State Oceanic Administration [201305002]
- National Natural Science Foundation of China [41571336]
- National Key R&D Program of China - ministry of Science and Technology of China [2017YFC0211904]
Ask authors/readers for more resources
Marine oil spills seriously impact the marine environment and transportation. When oil spill accidents occur, oil spill distribution information, in particular, the relative thickness of the oil film, is vital for emergency decision-making and cleaning. Hyperspectral remote sensing technology is an effective means to extract oil spill information. In this study, the concept of deep learning is introduced to the classification of oil film thickness based on hyperspectral remote sensing technology. According to the spatial and spectral characteristics, the stacked autoencoder network model based on the support vector machine is improved, enhancing the algorithm's classification accuracy in validating data sets. A method for classifying oil film thickness using the convolutional neural network is designed and implemented to solve the problem of space homogeneity and heterogeneity. Through numerous experiments and analyses, the potential of the two proposed deep learning methods for accurately classifying hyperspectral oil spill data is verified.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available