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Ground Truth Simulation for Deep Learning Classification of Mid-Resolution Venus Images Via Unmixing of High-Resolution Hyperspectral Fenix Data
Ground Truth Simulation for Deep Learning Classification of Mid-Resolution Venus Images Via Unmixing of High-Resolution Hyperspectral Fenix Data
- Source :
- IGARSS
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Training a deep neural network for classification constitutes a major problem in remote sensing due to the lack of adequate field data. Acquiring high-resolution ground truth (GT) by human interpretation is both cost-ineffective and inconsistent. We propose, instead, to utilize high-resolution, hyperspectral images for solving this problem, by unmixing these images to obtain reliable GT for training a deep network. Specifically, we simulate GT from high-resolution, hyperspectral FENIX images, and use it for training a convolutional neural network (CNN) for pixel-based classification. We show how the model can be transferred successfully to classify new mid-resolution VENuS imagery.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
0211 other engineering and technologies
Machine Learning (stat.ML)
Venus
02 engineering and technology
01 natural sciences
Convolutional neural network
Machine Learning (cs.LG)
Statistics - Machine Learning
FOS: Electrical engineering, electronic engineering, information engineering
Neural and Evolutionary Computing (cs.NE)
021101 geological & geomatics engineering
Ground truth
Pixel
Artificial neural network
biology
business.industry
Deep learning
Image and Video Processing (eess.IV)
010401 analytical chemistry
Computer Science - Neural and Evolutionary Computing
Hyperspectral imaging
Pattern recognition
Electrical Engineering and Systems Science - Image and Video Processing
Resolution (logic)
biology.organism_classification
0104 chemical sciences
Artificial intelligence
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
- Accession number :
- edsair.doi.dedup.....6dd73ed0eb78be0fd83d22ff04a98e7d
- Full Text :
- https://doi.org/10.1109/igarss.2019.8900186