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Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation
- Source :
- IEEE Access, Vol 7, Pp 81407-81418 (2019)
- Publication Year :
- 2019
- Publisher :
- arXiv, 2019.
-
Abstract
- Deep learning has been widely used for hyperspectral pixel classification due to its ability of generating deep feature representation. However, how to construct an efficient and powerful network suitable for hyperspectral data is still under exploration. In this paper, a novel neural network model is designed for taking full advantage of the spectral-spatial structure of hyperspectral data. Firstly, we extract pixel-based intrinsic features from rich yet redundant spectral bands by a subnetwork with supervised pre-training scheme. Secondly, in order to utilize the local spatial correlation among pixels, we share the previous subnetwork as a spectral feature extractor for each pixel in a patch of image, after which the spectral features of all pixels in a patch are combined and feeded into the subsequent classification subnetwork. Finally, the whole network is further fine-tuned to improve its classification performance. Specially, the spectral-spatial factorization scheme is applied in our model architecture, making the network size and the number of parameters great less than the existing spectral-spatial deep networks for hyperspectral image classification. Experiments on the hyperspectral data sets show that, compared with some state-of-art deep learning methods, our method achieves better classification results while having smaller network size and less parameters.<br />Comment: 12 pages, 10 figures
- Subjects :
- FOS: Computer and information sciences
General Computer Science
Computer Science - Artificial Intelligence
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
0211 other engineering and technologies
Hyperspectral pixel classification
02 engineering and technology
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
spectral-spatial feature factorization
021101 geological & geomatics engineering
Artificial neural network
Pixel
business.industry
Deep learning
General Engineering
Hyperspectral imaging
020207 software engineering
Pattern recognition
Spectral bands
Artificial Intelligence (cs.AI)
deep neural networks
Feature (computer vision)
Computer Science::Computer Vision and Pattern Recognition
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- IEEE Access, Vol 7, Pp 81407-81418 (2019)
- Accession number :
- edsair.doi.dedup.....657c49a2364d80f347a5ce1768e646be
- Full Text :
- https://doi.org/10.48550/arxiv.1904.07461