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Diversifying Deep Multiple Choices for Remote Sensing Scene Classification
- Source :
- IGARSS
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- Recently, deep models have shown powerful ability for remote sensing scene representation. However, the training process of these deep methods requires large amount of labelled samples while usual remote sensing image datasets cannot provide enough training samples. Therefore, the learned model is usually suboptimal. To solve the problem, this work focuses on obtaining multiple choices by training multiple models simultaneously, and then the human oracle can choose a proper one from these choices. However, training several models separately usually makes the obtained results similar. This paper tries to diversify the obtained choices by encouraging the obtained choices to repulse from each other. Experiments are conducted on Ucmerced Land Use dataset to validate the effectiveness of the proposed method to provide multiple diversified choices.
- Subjects :
- Land use
Computer science
Feature extraction
0211 other engineering and technologies
Entropy (information theory)
02 engineering and technology
010501 environmental sciences
01 natural sciences
Oracle
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
- Accession number :
- edsair.doi...........035e29d6dcfd6edb952e11c88c1f008e