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Multi-channel descriptors and ensemble of Extreme Learning Machines for classification of remote sensing images
- Source :
- Signal Processing: Image Communication. 39:111-120
- Publication Year :
- 2015
- Publisher :
- Elsevier BV, 2015.
-
Abstract
- We present a method for real-time scene classification which achieves high accuracy without a time consuming descriptor learning step and kernelized classifiers. Robustness of the classification is achieved by combining the powerful multi-channel Gabor-based descriptors and an ensemble of Extreme Learning Machines (ELM). We first extend the recently introduced Binary Gabor Patterns (BGP) to multi-channel images. This is done by extracting BGP over several color channels and embedding an additional compact color layout descriptor. Then we propose an effective method for the aggregation of multiple ELMs into a single classification system, which leads to significant classification accuracy improvements. The experimental evaluation demonstrates that multi-channel color information constantly improves classification results. The integration of multiple ELMs into an ensemble using the proposed aggregation strategy significantly outperforms linear SVM in terms of accuracy, and reaches results similar to the non-linear SVM while operating in real time. Therefore, an ensemble of ELMs with the proposed aggregation strategy could be used as an efficient alternative to the non-linear SVM for remote sensing image classification tasks.
- Subjects :
- Contextual image classification
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Binary number
Pattern recognition
computer.software_genre
Ensemble learning
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Robustness (computer science)
Computer Science::Computer Vision and Pattern Recognition
Color layout descriptor
Signal Processing
Embedding
Computer Vision and Pattern Recognition
Artificial intelligence
Data mining
Electrical and Electronic Engineering
business
computer
Software
Mathematics
Extreme learning machine
Remote sensing
Subjects
Details
- ISSN :
- 09235965
- Volume :
- 39
- Database :
- OpenAIRE
- Journal :
- Signal Processing: Image Communication
- Accession number :
- edsair.doi...........43c67a3210da19504ee7d5fe3fd0f0c0