Back to Search
Start Over
Detection and Correction of Mislabeled Training Samples for Hyperspectral Image Classification
- Source :
- IEEE Transactions on Geoscience and Remote Sensing. 56:5673-5686
- Publication Year :
- 2018
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2018.
-
Abstract
- In this paper, a novel method is introduced to detect and correct mislabeled training samples for hyperspectral image classification. First, domain transform recursive filtering-based feature extraction is used to improve the separability of the training samples. Then, constrained energy minimization-based object detection is performed on the training set with each training sample serving as the object spectrum. Finally, the label of each training sample is verified or corrected based on the averaged detection probabilities of different classes. Experiments performed on real hyperspectral data sets demonstrate the effectiveness of the proposed method in improving classification performance with respect to the classifier trained with the original training set that contains a number of mislabeled samples.
- Subjects :
- Training set
Computer science
business.industry
Feature extraction
0211 other engineering and technologies
Hyperspectral imaging
Pattern recognition
02 engineering and technology
Object detection
0202 electrical engineering, electronic engineering, information engineering
Hyperspectral image classification
General Earth and Planetary Sciences
020201 artificial intelligence & image processing
Artificial intelligence
Electrical and Electronic Engineering
business
Classifier (UML)
021101 geological & geomatics engineering
Subjects
Details
- ISSN :
- 15580644 and 01962892
- Volume :
- 56
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Geoscience and Remote Sensing
- Accession number :
- edsair.doi...........a9d38cb18bacd9ce527158d9893d6e56