Dual-Channel Densenet for Hyperspectral Image Classification
- Rochester Inst. of Technology, Rochester, NY (United States)
Deep neural networks provide deep extracted features for image classification. As a high dimension data, hyperspectral image (HSI) feature extraction is unlike an RGB image whose feature representation could not be simply generated in the spatial domain. To take full advantage of HSI, a dualchannel convolutional neural network (CNN) is applied, 1D convolution for the spectral domain and 2D convolution for spatial domain. For pixel-wise classification of HSI, in our network model, one-dimensional customized DenseNet is for extracting the hierarchical spectral features and another customized DenseNet is applied to extract the hierarchical spatial-related feature. Furthermore, we experimentally tuned the several widen factors and dense-net growth rates to evaluate the impact of hyper-parameter. Furthermore, to compare our proposed method with HSI classification methods, we test other three DNNs based method in two real-world HSI dataset. The result demonstrated our approach outperformed the state-of-art method
- Research Organization:
- Rochester Inst. of Technology, Rochester, NY (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
- DOE Contract Number:
- NA0002482
- OSTI ID:
- 1581642
- Journal Information:
- IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Proceedings (Online), Vol. 2018; Conference: 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018), Valencia (Spain), 22-27 Jul 2018; ISSN 2153-7003
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
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